{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "bc89a433-31ba-4007-934d-1117d5ab4bdf",
      "metadata": {
        "id": "bc89a433-31ba-4007-934d-1117d5ab4bdf"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import requests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "0ee1e123-daec-44e5-9eb5-f91809ac12bb",
      "metadata": {
        "id": "0ee1e123-daec-44e5-9eb5-f91809ac12bb"
      },
      "outputs": [],
      "source": [
        "DATA_DIR = \"data\"\n",
        "if not os.path.exists(DATA_DIR):\n",
        "    os.makedirs(DATA_DIR)\n",
        "URLS = {\n",
        "    'KDDTrain+.txt':'https://raw.githubusercontent.com/defcom17/NSL_KDD/master/KDDTrain+.txt',\n",
        "    'KDDTest+.txt' : 'https://raw.githubusercontent.com/defcom17/NSL_KDD/master/KDDTest+.txt'\n",
        "}\n",
        "\n",
        "def download_file(url,filename):\n",
        "    local_path = os.path.join(DATA_DIR, filename)\n",
        "\n",
        "    if os.path.exists(local_path):\n",
        "        print(f\"Correct file found: {filename} already exists\")\n",
        "        return\n",
        "    print(f\"Downloading {filename}...\")\n",
        "    try:\n",
        "        response = requests.get(url, stream=True)\n",
        "        response.raise_for_status()\n",
        "\n",
        "        with open(local_path, 'wb') as f:\n",
        "            for chunk in response.iter_content(chunk_size = 8000):\n",
        "                f.write(chunk)\n",
        "        print(f\"Success saved to {local_path}\")\n",
        "    except Exception as e:\n",
        "        print(f\"Failed to download {filename} :{e}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "6a5dbe4d-1317-4c5a-b178-2533de0958da",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6a5dbe4d-1317-4c5a-b178-2533de0958da",
        "outputId": "df911c4b-7c02-4654-ef31-2c50297f4010"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct file found: KDDTrain+.txt already exists\n",
            "Correct file found: KDDTest+.txt already exists\n",
            "\n",
            "All files should now be in 'data' folder.\n"
          ]
        }
      ],
      "source": [
        "for filename, url in URLS.items():\n",
        "    download_file(url,filename)\n",
        "print(\"\\nAll files should now be in 'data' folder.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "bc6b6652-2e64-4ddb-81fe-183083fc3e36",
      "metadata": {
        "id": "bc6b6652-2e64-4ddb-81fe-183083fc3e36"
      },
      "outputs": [],
      "source": [
        "# Step:0 import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "b90dada6-ae78-486e-b75f-21c617f728ee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "b90dada6-ae78-486e-b75f-21c617f728ee",
        "outputId": "bb38e7b9-da0e-4998-ff19-92e0c50e2cd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train shape: (125973, 43)\n",
            "Test shape: (22544, 43)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
              "0         0           tcp  ftp_data   SF        491          0     0   \n",
              "1         0           udp     other   SF        146          0     0   \n",
              "2         0           tcp   private   S0          0          0     0   \n",
              "3         0           tcp      http   SF        232       8153     0   \n",
              "4         0           tcp      http   SF        199        420     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
              "0               0       0    0  ...                    0.17   \n",
              "1               0       0    0  ...                    0.00   \n",
              "2               0       0    0  ...                    0.10   \n",
              "3               0       0    0  ...                    1.00   \n",
              "4               0       0    0  ...                    1.00   \n",
              "\n",
              "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
              "0                    0.03                         0.17   \n",
              "1                    0.60                         0.88   \n",
              "2                    0.05                         0.00   \n",
              "3                    0.00                         0.03   \n",
              "4                    0.00                         0.00   \n",
              "\n",
              "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
              "0                         0.00                  0.00   \n",
              "1                         0.00                  0.00   \n",
              "2                         0.00                  1.00   \n",
              "3                         0.04                  0.03   \n",
              "4                         0.00                  0.00   \n",
              "\n",
              "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
              "0                      0.00                  0.05                      0.00   \n",
              "1                      0.00                  0.00                      0.00   \n",
              "2                      1.00                  0.00                      0.00   \n",
              "3                      0.01                  0.00                      0.01   \n",
              "4                      0.00                  0.00                      0.00   \n",
              "\n",
              "   attack_type  level  \n",
              "0       normal     20  \n",
              "1       normal     15  \n",
              "2      neptune     19  \n",
              "3       normal     21  \n",
              "4       normal     21  \n",
              "\n",
              "[5 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-50bb3c6b-6537-46a5-b62b-b4483c42906d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>attack_type</th>\n",
              "      <th>level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>491</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>146</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>232</td>\n",
              "      <td>8153</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>199</td>\n",
              "      <td>420</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50bb3c6b-6537-46a5-b62b-b4483c42906d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-50bb3c6b-6537-46a5-b62b-b4483c42906d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-50bb3c6b-6537-46a5-b62b-b4483c42906d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "#Step1:Load Provide Dataset, Column names from NSL-KDD (full list)\n",
        "columns = [\n",
        "    'duration','protocol_type','service','flag','src_bytes','dst_bytes','land','wrong_fragment','urgent','hot',\n",
        "    'num_failed_logins','logged_in','num_compromised','root_shell','su_attempted','num_root','num_file_creations',\n",
        "    'num_shells','num_access_files','num_outbound_cmds','is_host_login','is_guest_login','count','srv_count',\n",
        "    'serror_rate','srv_serror_rate','rerror_rate','srv_rerror_rate','same_srv_rate','diff_srv_rate','srv_diff_host_rate',\n",
        "    'dst_host_count','dst_host_srv_count','dst_host_same_srv_rate','dst_host_diff_srv_rate','dst_host_same_src_port_rate',\n",
        "    'dst_host_srv_diff_host_rate','dst_host_serror_rate','dst_host_srv_serror_rate','dst_host_rerror_rate',\n",
        "    'dst_host_srv_rerror_rate','attack_type','level'\n",
        "]\n",
        "\n",
        "train_path = os.path.join(DATA_DIR, 'KDDTrain+.txt')\n",
        "test_path = os.path.join(DATA_DIR, 'KDDTest+.txt')\n",
        "\n",
        "# Read CSV with our column names\n",
        "train_df = pd.read_csv(train_path, names=columns)\n",
        "test_df = pd.read_csv(test_path, names=columns)\n",
        "\n",
        "print(\"Train shape:\", train_df.shape)\n",
        "print(\"Test shape:\", test_df.shape)\n",
        "train_df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "6b72bcb5-e3a5-4f0d-a434-86cd3d2641ea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b72bcb5-e3a5-4f0d-a434-86cd3d2641ea",
        "outputId": "ee74e9d7-c4c0-466f-c794-e99b35a8cf6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Attack type counts:\n",
            " attack_type\n",
            "normal             67343\n",
            "neptune            41214\n",
            "satan               3633\n",
            "ipsweep             3599\n",
            "portsweep           2931\n",
            "smurf               2646\n",
            "nmap                1493\n",
            "back                 956\n",
            "teardrop             892\n",
            "warezclient          890\n",
            "pod                  201\n",
            "guess_passwd          53\n",
            "buffer_overflow       30\n",
            "warezmaster           20\n",
            "land                  18\n",
            "imap                  11\n",
            "rootkit               10\n",
            "loadmodule             9\n",
            "ftp_write              8\n",
            "multihop               7\n",
            "phf                    4\n",
            "perl                   3\n",
            "spy                    2\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#Step 2:Slice Data(TCP Traffic,Normal/DOS) before tht we will check what are the unique attack types in the training data\n",
        "attack_counts=train_df['attack_type'].value_counts()\n",
        "print(\"Attack type counts:\\n\",attack_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "0b609da5-f0d3-48ab-a952-7df18aa93692",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0b609da5-f0d3-48ab-a952-7df18aa93692",
        "outputId": "b42bac80-d058-4abb-92db-88c60c0a5496"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "attack_category\n",
            "normal    67343\n",
            "dos       45927\n",
            "probe     11656\n",
            "r2l         998\n",
            "u2r          49\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#to make it easier of ML,we can create a new column attack_category\n",
        "def categorize_attack(x):\n",
        "    if x=='normal':\n",
        "        return 'normal'\n",
        "    elif x in['neptune','smurf','back','teardrop','pod','land']:\n",
        "        return 'dos'\n",
        "    elif x in ['satan','ipsweep','portsweep','nmap']:\n",
        "        return 'probe' #scanning attacks\n",
        "    elif x in ['guess_passwd','ftp_write','warezclient','warezmaster','phf','multihop','imap','spy','perl']:\n",
        "        return 'r2l'  #Remote to Local\n",
        "    elif x in ['buffer_overflow','loadmodule','rootkit']:\n",
        "        return 'u2r' #User to Root\n",
        "    else:\n",
        "        return 'other'\n",
        "\n",
        "train_df['attack_category'] = train_df['attack_type'].apply(categorize_attack)\n",
        "test_df['attack_category'] = test_df['attack_type'].apply(categorize_attack)\n",
        "print(train_df['attack_category'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "u8YffmNj76Z_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "u8YffmNj76Z_",
        "outputId": "8bce925f-6c97-4d44-bb4e-44b808e8f6c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Protocol vs Attack Category:\n",
            " attack_category    dos  normal  probe  r2l  u2r\n",
            "protocol_type                                  \n",
            "icmp              2847    1309   4135    0    0\n",
            "tcp              42188   53600   5857  998   46\n",
            "udp                892   12434   1664    0    3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ===============================\n",
        "# My Exploration — Protocol vs Attack Category\n",
        "# ===============================\n",
        "\n",
        "proto_attack = pd.crosstab(train_df['protocol_type'], train_df['attack_category'])\n",
        "\n",
        "print(\"Protocol vs Attack Category:\\n\", proto_attack)\n",
        "\n",
        "proto_attack.plot(kind='bar', stacked=True, figsize=(8,5))\n",
        "plt.title(\"Protocol Distribution Across Attack Categories\")\n",
        "plt.xlabel(\"Protocol\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "c98fe79e-27e0-4dd9-b23b-b033701e3698",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c98fe79e-27e0-4dd9-b23b-b033701e3698",
        "outputId": "11edb99e-850f-4020-eda2-eafaf9bbd4a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Binary Train shape: (95788, 44)\n",
            "attack_category\n",
            "normal    53600\n",
            "dos       42188\n",
            "Name: count, dtype: int64\n",
            "Binary Test shape: (12865, 44)\n",
            "attack_category\n",
            "normal    7842\n",
            "dos       5023\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Step 3: Keep only TCP traffic\n",
        "tcp_train = train_df[train_df['protocol_type'] == 'tcp']\n",
        "tcp_test = test_df[test_df['protocol_type'] == 'tcp']\n",
        "\n",
        "# Step 4: Binary classification: normal vs DOS attacks\n",
        "binary_train = tcp_train[tcp_train['attack_category'].isin(['normal','dos'])]\n",
        "binary_test = tcp_test[tcp_test['attack_category'].isin(['normal','dos'])]\n",
        "\n",
        "# Quick check\n",
        "print(\"Binary Train shape:\", binary_train.shape)\n",
        "print(binary_train['attack_category'].value_counts())\n",
        "\n",
        "print(\"Binary Test shape:\", binary_test.shape)\n",
        "print(binary_test['attack_category'].value_counts())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "jIjibNv_8Cjv",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jIjibNv_8Cjv",
        "outputId": "54e9c556-2c6d-40a8-a702-1bcc26920920"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ===============================\n",
        "# My Exploration — Feature Distribution: Normal vs DoS\n",
        "# ===============================\n",
        "\n",
        "features = ['duration','src_bytes','dst_bytes']\n",
        "\n",
        "for f in features:\n",
        "    plt.figure(figsize=(6,4))\n",
        "    sns.kdeplot(data=binary_train, x=f, hue='attack_category', common_norm=False)\n",
        "    plt.title(f\"{f} Distribution: Normal vs DoS\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "ffc14942-e102-4325-a2cd-92684b1ec187",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "ffc14942-e102-4325-a2cd-92684b1ec187",
        "outputId": "33d21cff-f56e-48ed-bdff-baf0f589b8b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "attack_category\n",
              "normal    0.559569\n",
              "dos       0.440431\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>attack_category</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>normal</th>\n",
              "      <td>0.559569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dos</th>\n",
              "      <td>0.440431</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "#should check balance or to check proportion on normal vs DOS\n",
        "binary_train['attack_category'].value_counts(normalize=True)\n",
        "#so abot 56% normal and 44% DOS-not heavily imbalanced,so Random Forest or similar classifiers will work well\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "82555c88-0de6-4a35-9cd7-76e4e5bfcfbe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82555c88-0de6-4a35-9cd7-76e4e5bfcfbe",
        "outputId": "f1f80237-92ca-45ef-c6e1-fe9b663b8cf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Average features per attack category:\n",
            "                   duration     src_bytes    dst_bytes\n",
            "attack_category                                      \n",
            "dos               0.006779   1216.122736   184.196051\n",
            "normal           76.044813  16480.340672  5420.045802\n"
          ]
        }
      ],
      "source": [
        "# Step 5: Aggregate / Roll-up by attack_category\n",
        "agg_category = binary_train.groupby('attack_category')[['duration','src_bytes','dst_bytes']].mean()\n",
        "\n",
        "print(\"\\nAverage features per attack category:\\n\", agg_category)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "84d68a6b-6477-4eea-a629-597c7d108636",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "84d68a6b-6477-4eea-a629-597c7d108636",
        "outputId": "2a5a6f50-dd25-4d1e-b7ac-94a081bfd48a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Bar plot of Average features by Attack Category\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Aggregate average features per attack category\n",
        "agg_category = binary_train.groupby('attack_category')[['duration','src_bytes','dst_bytes']].mean()\n",
        "agg_category = agg_category.reset_index()\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "# Plot duration\n",
        "plt.bar(agg_category['attack_category'], agg_category['duration'], color=['skyblue','salmon'])\n",
        "plt.title(\"Average Connection Duration by Attack Category\")\n",
        "plt.xlabel(\"Attack Category\")\n",
        "plt.ylabel(\"Average Duration\")\n",
        "plt.show()\n",
        "\n",
        "# Plot src_bytes\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.bar(agg_category['attack_category'], agg_category['src_bytes'], color=['skyblue','salmon'])\n",
        "plt.title(\"Average Source Bytes by Attack Category\")\n",
        "plt.xlabel(\"Attack Category\")\n",
        "plt.ylabel(\"Average src_bytes\")\n",
        "plt.show()\n",
        "\n",
        "# Plot dst_bytes\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.bar(agg_category['attack_category'], agg_category['dst_bytes'], color=['skyblue','salmon'])\n",
        "plt.title(\"Average Destination Bytes by Attack Category\")\n",
        "plt.xlabel(\"Attack Category\")\n",
        "plt.ylabel(\"Average dst_bytes\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "D5OSgDIu8fLV",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "D5OSgDIu8fLV",
        "outputId": "a04846d7-033b-4364-93e0-bbe93ef42438"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ===============================\n",
        "# My Exploration — Duration by Attack Category\n",
        "# ===============================\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.boxplot(x=binary_train['attack_category'], y=binary_train['duration'])\n",
        "plt.yscale('log')\n",
        "plt.title(\"Connection Duration by Attack Category\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "7a6d963f-ebf2-4ef3-83f8-6f0460ee9ebc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "7a6d963f-ebf2-4ef3-83f8-6f0460ee9ebc",
        "outputId": "ed2b51ba-6527-4ad6-da8c-ef81f202d6c1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Aggregate average features per attack category\n",
        "agg_category = binary_train.groupby('attack_category')[['duration','src_bytes','dst_bytes']].mean()\n",
        "agg_category = agg_category.reset_index()\n",
        "\n",
        "# Apply log scale (log1p to handle zeros)\n",
        "agg_category_log = agg_category.copy()\n",
        "agg_category_log[['duration','src_bytes','dst_bytes']] = np.log1p(agg_category_log[['duration','src_bytes','dst_bytes']])\n",
        "\n",
        "# Plot all three features in one grouped bar chart\n",
        "features = ['duration','src_bytes','dst_bytes']\n",
        "x = np.arange(len(agg_category_log['attack_category']))  # positions for attack categories\n",
        "width = 0.25  # width of each bar\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "# Plot each feature\n",
        "plt.bar(x - width, agg_category_log['duration'], width, label='Duration', color='skyblue')\n",
        "plt.bar(x, agg_category_log['src_bytes'], width, label='Src Bytes', color='salmon')\n",
        "plt.bar(x + width, agg_category_log['dst_bytes'], width, label='Dst Bytes', color='lightgreen')\n",
        "\n",
        "# Labels and title\n",
        "plt.xticks(x, agg_category_log['attack_category'])\n",
        "plt.ylabel('Log(Average Value + 1)')\n",
        "plt.title('Comparison of Average Features by Attack Category (Log Scale)')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "bd28b99b-6a6d-469f-8d64-2df5ff82854d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bd28b99b-6a6d-469f-8d64-2df5ff82854d",
        "outputId": "15118184-907a-4047-808a-86ba32a42a0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preprocessing completed. Derived datasets saved!\n",
            "Binary Train shape: (95788, 44)\n",
            "Binary Test shape: (12865, 44)\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "\n",
        "# Columns to encode\n",
        "categorical_cols = ['protocol_type', 'service', 'flag']\n",
        "\n",
        "categorical_cols = ['protocol_type', 'service', 'flag']\n",
        "\n",
        "for col in categorical_cols:\n",
        "    # Combine train + test to ensure all labels are captured\n",
        "    combined = pd.concat([binary_train[col], binary_test[col]], axis=0)\n",
        "    codes, uniques = pd.factorize(combined)\n",
        "\n",
        "    # Split back into train and test\n",
        "    binary_train[col] = codes[:len(binary_train)]\n",
        "    binary_test[col] = codes[len(binary_train):]\n",
        "\n",
        "\n",
        "# Columns to normalize\n",
        "numeric_cols = ['duration', 'src_bytes', 'dst_bytes']\n",
        "\n",
        "# StandardScaler for numerical features\n",
        "scaler = StandardScaler()\n",
        "binary_train[numeric_cols] = scaler.fit_transform(binary_train[numeric_cols])\n",
        "binary_test[numeric_cols] = scaler.transform(binary_test[numeric_cols])\n",
        "\n",
        "# Save the processed datasets for reuse\n",
        "binary_train.to_csv('derived_train_attack_category.csv', index=False)\n",
        "binary_test.to_csv('derived_test_attack_category.csv', index=False)\n",
        "\n",
        "print(\"Preprocessing completed. Derived datasets saved!\")\n",
        "print(\"Binary Train shape:\", binary_train.shape)\n",
        "print(\"Binary Test shape:\", binary_test.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "cf86ed1c-d2b8-4b71-8172-232f95695798",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cf86ed1c-d2b8-4b71-8172-232f95695798",
        "outputId": "933ada5e-bfdb-40ae-849e-b508c6a6352a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train shape: (95788, 44)\n",
            "Test shape: (12865, 44)\n",
            "Number of features: 41\n",
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         dos       1.00      1.00      1.00      5023\n",
            "      normal       1.00      1.00      1.00      7842\n",
            "\n",
            "    accuracy                           1.00     12865\n",
            "   macro avg       1.00      1.00      1.00     12865\n",
            "weighted avg       1.00      1.00      1.00     12865\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of misclassified samples: 34\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Step 1: Load preprocessed datasets\n",
        "train_df = pd.read_csv('derived_train_attack_category.csv')\n",
        "test_df = pd.read_csv('derived_test_attack_category.csv')\n",
        "\n",
        "print(\"Train shape:\", train_df.shape)\n",
        "print(\"Test shape:\", test_df.shape)\n",
        "\n",
        "# Step 2: Prepare features and target\n",
        "X_train = train_df.drop(['attack_type','attack_category','level'], axis=1)\n",
        "y_train = train_df['attack_category']\n",
        "\n",
        "X_test = test_df.drop(['attack_type','attack_category','level'], axis=1)\n",
        "y_test = test_df['attack_category']\n",
        "\n",
        "print(\"Number of features:\", X_train.shape[1])\n",
        "\n",
        "# Step 3: Train Random Forest Classifier\n",
        "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Step 4: Predict on Test Data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Step 5: Evaluate Model\n",
        "print(\"\\nClassification Report:\\n\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Confusion Matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(6,5))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['normal','dos'], yticklabels=['normal','dos'])\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()\n",
        "\n",
        "# ===============================\n",
        "# My Exploration — Misclassified Samples\n",
        "# ===============================\n",
        "\n",
        "errors = X_test.copy()\n",
        "errors['actual'] = y_test\n",
        "errors['pred'] = y_pred\n",
        "\n",
        "misclassified = errors[errors['actual'] != errors['pred']]\n",
        "\n",
        "print(\"Number of misclassified samples:\", len(misclassified))\n",
        "misclassified.head()\n",
        "\n",
        "# Step 6: Feature Importance\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Get importance values from model\n",
        "importances = model.feature_importances_\n",
        "\n",
        "# Create Series with feature names\n",
        "feat_importances = pd.Series(importances, index=X_train.columns)\n",
        "\n",
        "# Sort and select top 10\n",
        "feat_importances = feat_importances.sort_values(ascending=False).head(10)\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "sns.barplot(x=feat_importances.values,\n",
        "            y=feat_importances.index,\n",
        "            color='skyblue')\n",
        "\n",
        "plt.title(\"Top 10 Feature Importances (Random Forest)\")\n",
        "plt.xlabel(\"Importance\")\n",
        "plt.ylabel(\"Feature\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "nmlIc1pb84Au",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nmlIc1pb84Au",
        "outputId": "378d0cb9-07ee-45d9-c517-19f069811bf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most important features influencing attack detection:\n",
            "- same_srv_rate\n",
            "- src_bytes\n",
            "- diff_srv_rate\n",
            "- flag\n",
            "- count\n",
            "- logged_in\n",
            "- serror_rate\n",
            "- dst_host_same_srv_rate\n",
            "- srv_serror_rate\n",
            "- dst_host_serror_rate\n"
          ]
        }
      ],
      "source": [
        "# ===============================\n",
        "# My Interpretation — Important Features\n",
        "# ===============================\n",
        "\n",
        "important_features = feat_importances.index.tolist()\n",
        "\n",
        "print(\"Most important features influencing attack detection:\")\n",
        "for f in important_features:\n",
        "    print(\"-\", f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "d715ff99-0ddc-4b19-b731-5e28c8c47cfa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d715ff99-0ddc-4b19-b731-5e28c8c47cfa",
        "outputId": "e7ac2aee-6a1b-4ddc-fc7d-bb13c20d40e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "attack_category\n",
            "normal    53600\n",
            "dos       42188\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "def categorize_attack(x):\n",
        "    if x=='normal':\n",
        "        return 'normal'\n",
        "    elif x in['neptune','smurf','back','teardrop','pod','land']:\n",
        "        return 'dos'\n",
        "    elif x in ['satan','ipsweep','portsweep','nmap']:\n",
        "        return 'probe' #scanning attacks\n",
        "    elif x in ['guess_passwd','ftp_write','warezclient','warezmaster','phf','multihop','imap','spy','perl']:\n",
        "        return 'r2l'  #Remote to Local\n",
        "    elif x in ['buffer_overflow','loadmodule','rootkit']:\n",
        "        return 'u2r' #User to Root\n",
        "    else:\n",
        "        return 'other'\n",
        "\n",
        "train_df['attack_category'] = train_df['attack_type'].apply(categorize_attack)\n",
        "test_df['attack_category'] = test_df['attack_type'].apply(categorize_attack)\n",
        "print(train_df['attack_category'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "4a244db9-4df3-4030-903a-1e49b2496581",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4a244db9-4df3-4030-903a-1e49b2496581",
        "outputId": "a4c65188-114d-4f66-e1b2-e1c7a31836c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical Features: 3\n",
            "Binary Features: 4\n",
            "Numerical Features: 36\n",
            "       duration  protocol_type  service   flag  src_bytes  dst_bytes   land  \\\n",
            "0         False          False    False  False      False      False  False   \n",
            "1         False          False    False  False      False      False  False   \n",
            "2         False          False    False  False      False      False  False   \n",
            "3         False          False    False  False      False      False  False   \n",
            "4         False          False    False  False      False      False  False   \n",
            "...         ...            ...      ...    ...        ...        ...    ...   \n",
            "95783     False          False    False  False      False      False  False   \n",
            "95784     False          False    False  False      False      False  False   \n",
            "95785     False          False    False  False      False      False  False   \n",
            "95786     False          False    False  False      False      False  False   \n",
            "95787     False          False    False  False      False      False  False   \n",
            "\n",
            "       wrong_fragment  urgent    hot  ...  dst_host_diff_srv_rate  \\\n",
            "0               False   False  False  ...                   False   \n",
            "1               False   False  False  ...                   False   \n",
            "2               False   False  False  ...                   False   \n",
            "3               False   False  False  ...                   False   \n",
            "4               False   False  False  ...                   False   \n",
            "...               ...     ...    ...  ...                     ...   \n",
            "95783           False   False  False  ...                   False   \n",
            "95784           False   False  False  ...                   False   \n",
            "95785           False   False  False  ...                   False   \n",
            "95786           False   False  False  ...                   False   \n",
            "95787           False   False  False  ...                   False   \n",
            "\n",
            "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
            "0                            False                        False   \n",
            "1                            False                        False   \n",
            "2                            False                        False   \n",
            "3                            False                        False   \n",
            "4                            False                        False   \n",
            "...                            ...                          ...   \n",
            "95783                        False                        False   \n",
            "95784                        False                        False   \n",
            "95785                        False                        False   \n",
            "95786                        False                        False   \n",
            "95787                        False                        False   \n",
            "\n",
            "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
            "0                     False                     False                 False   \n",
            "1                     False                     False                 False   \n",
            "2                     False                     False                 False   \n",
            "3                     False                     False                 False   \n",
            "4                     False                     False                 False   \n",
            "...                     ...                       ...                   ...   \n",
            "95783                 False                     False                 False   \n",
            "95784                 False                     False                 False   \n",
            "95785                 False                     False                 False   \n",
            "95786                 False                     False                 False   \n",
            "95787                 False                     False                 False   \n",
            "\n",
            "       dst_host_srv_rerror_rate  attack_type  level  attack_category  \n",
            "0                         False        False  False            False  \n",
            "1                         False        False  False            False  \n",
            "2                         False        False  False            False  \n",
            "3                         False        False  False            False  \n",
            "4                         False        False  False            False  \n",
            "...                         ...          ...    ...              ...  \n",
            "95783                     False        False  False            False  \n",
            "95784                     False        False  False            False  \n",
            "95785                     False        False  False            False  \n",
            "95786                     False        False  False            False  \n",
            "95787                     False        False  False            False  \n",
            "\n",
            "[95788 rows x 44 columns]\n"
          ]
        }
      ],
      "source": [
        "#Definign feature Groups\n",
        "\n",
        "categorical_features = ['protocol_type','service','flag']\n",
        "binary_features = ['land','logged_in','is_host_login','is_guest_login']\n",
        "#All the other numerical values\n",
        "\n",
        "numerical_features = [col for col in columns if col not in categorical_features + binary_features +['label','difficulty_level']]\n",
        "\n",
        "print(f\"Categorical Features: {len(categorical_features)}\")\n",
        "print(f\"Binary Features: {len(binary_features)}\")\n",
        "print(f\"Numerical Features: {len(numerical_features)}\")\n",
        "\n",
        "print(train_df.isnull())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "d1ddb1d2-ac84-47d7-a6ae-5011e1857468",
      "metadata": {
        "id": "d1ddb1d2-ac84-47d7-a6ae-5011e1857468"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "import warnings\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, LabelEncoder, OneHotEncoder, PowerTransformer\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "# from sklearn.feature_selection import SelectKBest, chi2, f_classif, RFE, MutualInfoClassifier\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.ensemble import RandomForestClassifier, IsolationForest\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer, KNNImputer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "a3ba5593-0d35-4d4f-b5e4-c85a2bdb61e2",
      "metadata": {
        "id": "a3ba5593-0d35-4d4f-b5e4-c85a2bdb61e2"
      },
      "outputs": [],
      "source": [
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Plot styling\n",
        "plt.style.use('ggplot')\n",
        "sns.set_palette(\"husl\")\n",
        "\n",
        "# Define Column Names for NSL-KDD (Standard List)\n",
        "COL_NAMES = [\"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\n",
        "    \"dst_bytes\",\"land\",\"wrong_fragment\",\"urgent\",\"hot\",\"num_failed_logins\",\n",
        "    \"logged_in\",\"num_compromised\",\"root_shell\",\"su_attempted\",\"num_root\",\n",
        "    \"num_file_creations\",\"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\n",
        "    \"is_host_login\",\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\",\n",
        "    \"srv_serror_rate\",\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\",\n",
        "    \"diff_srv_rate\",\"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\n",
        "    \"dst_host_same_srv_rate\",\"dst_host_diff_srv_rate\",\"dst_host_same_src_port_rate\",\n",
        "    \"dst_host_srv_diff_host_rate\",\"dst_host_serror_rate\",\"dst_host_srv_serror_rate\",\n",
        "    \"dst_host_rerror_rate\",\"dst_host_srv_rerror_rate\",\"label\", \"difficulty_level\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "852f694c-86f0-4fa3-9f93-9092d59f35d3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "852f694c-86f0-4fa3-9f93-9092d59f35d3",
        "outputId": "d971ea8e-2238-485c-add7-6bd567cc3419"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Shape:    duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
            "0         0           tcp  ftp_data   SF        491          0     0   \n",
            "1         0           udp     other   SF        146          0     0   \n",
            "2         0           tcp   private   S0          0          0     0   \n",
            "3         0           tcp      http   SF        232       8153     0   \n",
            "4         0           tcp      http   SF        199        420     0   \n",
            "\n",
            "   wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
            "0               0       0    0  ...                    0.17   \n",
            "1               0       0    0  ...                    0.00   \n",
            "2               0       0    0  ...                    0.10   \n",
            "3               0       0    0  ...                    1.00   \n",
            "4               0       0    0  ...                    1.00   \n",
            "\n",
            "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
            "0                    0.03                         0.17   \n",
            "1                    0.60                         0.88   \n",
            "2                    0.05                         0.00   \n",
            "3                    0.00                         0.03   \n",
            "4                    0.00                         0.00   \n",
            "\n",
            "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
            "0                         0.00                  0.00   \n",
            "1                         0.00                  0.00   \n",
            "2                         0.00                  1.00   \n",
            "3                         0.04                  0.03   \n",
            "4                         0.00                  0.00   \n",
            "\n",
            "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
            "0                      0.00                  0.05                      0.00   \n",
            "1                      0.00                  0.00                      0.00   \n",
            "2                      1.00                  0.00                      0.00   \n",
            "3                      0.01                  0.00                      0.01   \n",
            "4                      0.00                  0.00                      0.00   \n",
            "\n",
            "     label  difficulty_level  \n",
            "0   normal                20  \n",
            "1   normal                15  \n",
            "2  neptune                19  \n",
            "3   normal                21  \n",
            "4   normal                21  \n",
            "\n",
            "[5 rows x 43 columns]\n",
            "Test Data Shape:        duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
            "22539         0           tcp      smtp   SF        794        333     0   \n",
            "22540         0           tcp      http   SF        317        938     0   \n",
            "22541         0           tcp      http   SF      54540       8314     0   \n",
            "22542         0           udp  domain_u   SF         42         42     0   \n",
            "22543         0           tcp    sunrpc  REJ          0          0     0   \n",
            "\n",
            "       wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
            "22539               0       0    0  ...                    0.72   \n",
            "22540               0       0    0  ...                    1.00   \n",
            "22541               0       0    2  ...                    1.00   \n",
            "22542               0       0    0  ...                    0.99   \n",
            "22543               0       0    0  ...                    0.08   \n",
            "\n",
            "       dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
            "22539                    0.06                         0.01   \n",
            "22540                    0.00                         0.01   \n",
            "22541                    0.00                         0.00   \n",
            "22542                    0.01                         0.00   \n",
            "22543                    0.03                         0.00   \n",
            "\n",
            "       dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
            "22539                         0.01                  0.01   \n",
            "22540                         0.01                  0.01   \n",
            "22541                         0.00                  0.00   \n",
            "22542                         0.00                  0.00   \n",
            "22543                         0.00                  0.00   \n",
            "\n",
            "       dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
            "22539                       0.0                  0.00   \n",
            "22540                       0.0                  0.00   \n",
            "22541                       0.0                  0.07   \n",
            "22542                       0.0                  0.00   \n",
            "22543                       0.0                  0.44   \n",
            "\n",
            "       dst_host_srv_rerror_rate   label  difficulty_level  \n",
            "22539                      0.00  normal                21  \n",
            "22540                      0.00  normal                21  \n",
            "22541                      0.07    back                15  \n",
            "22542                      0.00  normal                21  \n",
            "22543                      1.00   mscan                14  \n",
            "\n",
            "[5 rows x 43 columns]\n"
          ]
        }
      ],
      "source": [
        "train_path = './data/KDDTrain+.txt'\n",
        "test_path = './data/KDDTest+.txt'\n",
        "\n",
        "# Try loading with error handling for paths\n",
        "try:\n",
        "    df_train = pd.read_csv(train_path, header=None, names=COL_NAMES)\n",
        "    df_test = pd.read_csv(test_path, header=None, names=COL_NAMES)\n",
        "    print(f\"Training Data Shape: {df_train.head()}\")\n",
        "    print(f\"Test Data Shape: {df_test.tail()}\")\n",
        "except FileNotFoundError:\n",
        "    print(\"Files not found. Please check the paths. Trying absolute paths or checking local directory...\")\n",
        "    # Fallback to local directory if paths are different\n",
        "    if os.path.exists('KDDTrain+.txt'):\n",
        "        df_train = pd.read_csv('KDDTrain+.txt', header=None, names=COL_NAMES)\n",
        "        df_test = pd.read_csv('KDDTest+.txt', header=None, names=COL_NAMES)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "2ec55113-7b5a-4d8a-a020-ec6198705dc8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ec55113-7b5a-4d8a-a020-ec6198705dc8",
        "outputId": "5cbeb7cc-353a-4418-8f18-4590b9c3735b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical Features: 3\n",
            "Binary Features: 4\n",
            "Numerical Features: 34\n",
            "        duration  protocol_type  service   flag  src_bytes  dst_bytes   land  \\\n",
            "0          False          False    False  False      False      False  False   \n",
            "1          False          False    False  False      False      False  False   \n",
            "2          False          False    False  False      False      False  False   \n",
            "3          False          False    False  False      False      False  False   \n",
            "4          False          False    False  False      False      False  False   \n",
            "...          ...            ...      ...    ...        ...        ...    ...   \n",
            "125968     False          False    False  False      False      False  False   \n",
            "125969     False          False    False  False      False      False  False   \n",
            "125970     False          False    False  False      False      False  False   \n",
            "125971     False          False    False  False      False      False  False   \n",
            "125972     False          False    False  False      False      False  False   \n",
            "\n",
            "        wrong_fragment  urgent    hot  ...  dst_host_same_srv_rate  \\\n",
            "0                False   False  False  ...                   False   \n",
            "1                False   False  False  ...                   False   \n",
            "2                False   False  False  ...                   False   \n",
            "3                False   False  False  ...                   False   \n",
            "4                False   False  False  ...                   False   \n",
            "...                ...     ...    ...  ...                     ...   \n",
            "125968           False   False  False  ...                   False   \n",
            "125969           False   False  False  ...                   False   \n",
            "125970           False   False  False  ...                   False   \n",
            "125971           False   False  False  ...                   False   \n",
            "125972           False   False  False  ...                   False   \n",
            "\n",
            "        dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
            "0                        False                        False   \n",
            "1                        False                        False   \n",
            "2                        False                        False   \n",
            "3                        False                        False   \n",
            "4                        False                        False   \n",
            "...                        ...                          ...   \n",
            "125968                   False                        False   \n",
            "125969                   False                        False   \n",
            "125970                   False                        False   \n",
            "125971                   False                        False   \n",
            "125972                   False                        False   \n",
            "\n",
            "        dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
            "0                             False                 False   \n",
            "1                             False                 False   \n",
            "2                             False                 False   \n",
            "3                             False                 False   \n",
            "4                             False                 False   \n",
            "...                             ...                   ...   \n",
            "125968                        False                 False   \n",
            "125969                        False                 False   \n",
            "125970                        False                 False   \n",
            "125971                        False                 False   \n",
            "125972                        False                 False   \n",
            "\n",
            "        dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
            "0                          False                 False   \n",
            "1                          False                 False   \n",
            "2                          False                 False   \n",
            "3                          False                 False   \n",
            "4                          False                 False   \n",
            "...                          ...                   ...   \n",
            "125968                     False                 False   \n",
            "125969                     False                 False   \n",
            "125970                     False                 False   \n",
            "125971                     False                 False   \n",
            "125972                     False                 False   \n",
            "\n",
            "        dst_host_srv_rerror_rate  label  difficulty_level  \n",
            "0                          False  False             False  \n",
            "1                          False  False             False  \n",
            "2                          False  False             False  \n",
            "3                          False  False             False  \n",
            "4                          False  False             False  \n",
            "...                          ...    ...               ...  \n",
            "125968                     False  False             False  \n",
            "125969                     False  False             False  \n",
            "125970                     False  False             False  \n",
            "125971                     False  False             False  \n",
            "125972                     False  False             False  \n",
            "\n",
            "[125973 rows x 43 columns]\n"
          ]
        }
      ],
      "source": [
        "categorical_features = ['protocol_type','service','flag']\n",
        "binary_features = ['land','logged_in','is_host_login','is_guest_login']\n",
        "#All the other numerical values\n",
        "\n",
        "numerical_features = [col for col in COL_NAMES if col not in categorical_features + binary_features +['label','difficulty_level']]\n",
        "\n",
        "print(f\"Categorical Features: {len(categorical_features)}\")\n",
        "print(f\"Binary Features: {len(binary_features)}\")\n",
        "print(f\"Numerical Features: {len(numerical_features)}\")\n",
        "\n",
        "print(df_train.isnull())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "0eddb800-6a8c-4c11-b376-ee78f90cecf6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0eddb800-6a8c-4c11-b376-ee78f90cecf6",
        "outputId": "eb863906-a9c8-444b-b526-6b60ab8e3e6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Identifies 1256 Outlilers using the Isolation forest\n"
          ]
        }
      ],
      "source": [
        "def clean_outliers_isolation_forest(df,contamination = 0.01):\n",
        "    iso_df  = df[numerical_features].copy()\n",
        "\n",
        "    clf = IsolationForest(max_samples='auto',contamination = contamination, random_state = 42)\n",
        "\n",
        "    outliers = clf.fit_predict(iso_df)\n",
        "\n",
        "    n_outliers = (outliers == -1).sum()\n",
        "    print(f\"Identifies {n_outliers} Outlilers using the Isolation forest\")\n",
        "\n",
        "    return outliers\n",
        "df_train['is_outlier'] = clean_outliers_isolation_forest(df_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "id": "6e8e6a98-2c11-429e-8935-83e0d67af323",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6e8e6a98-2c11-429e-8935-83e0d67af323",
        "outputId": "652ef354-f08e-4ed0-ec57-b9874230c4ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Class Distribution:\n",
            "attack_class\n",
            "normal    67343\n",
            "DoS       45927\n",
            "Probe     11656\n",
            "R2L         995\n",
            "U2R          52\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Mapping Dictionary\n",
        "attack_mapping = {\n",
        "    'normal': 'normal',\n",
        "\n",
        "    # DoS\n",
        "    'back': 'DoS', 'land': 'DoS', 'neptune': 'DoS', 'pod': 'DoS', 'smurf': 'DoS', 'teardrop': 'DoS', 'mailbomb': 'DoS', 'apache2': 'DoS', 'processtable': 'DoS', 'udpstorm': 'DoS',\n",
        "\n",
        "    # Probe\n",
        "    'satan': 'Probe', 'ipsweep': 'Probe', 'nmap': 'Probe', 'portsweep': 'Probe', 'mscan': 'Probe', 'saint': 'Probe',\n",
        "\n",
        "    # R2L\n",
        "    'guess_passwd': 'R2L', 'ftp_write': 'R2L', 'imap': 'R2L', 'phf': 'R2L', 'multihop': 'R2L', 'warezmaster': 'R2L', 'warezclient': 'R2L', 'spy': 'R2L', 'xlock': 'R2L', 'xsnoop': 'R2L', 'snmpguess': 'R2L', 'snmpgetattack': 'R2L', 'httptunnel': 'R2L', 'sendmail': 'R2L', 'named': 'R2L',\n",
        "\n",
        "    # U2R\n",
        "    'buffer_overflow': 'U2R', 'loadmodule': 'U2R', 'perl': 'U2R', 'rootkit': 'U2R', 'ps': 'U2R', 'xterm': 'U2R', 'sqlattack': 'U2R'\n",
        "}\n",
        "\n",
        "# Apply mapping\n",
        "df_train['attack_class'] = df_train['label'].map(lambda x: attack_mapping.get(x, 'other'))\n",
        "df_test['attack_class'] = df_test['label'].map(lambda x: attack_mapping.get(x, 'other'))\n",
        "\n",
        "print(\"Training Class Distribution:\")\n",
        "print(df_train['attack_class'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "a5483f74-a9c6-445b-a8b1-c3e5ef5e26ef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "a5483f74-a9c6-445b-a8b1-c3e5ef5e26ef",
        "outputId": "9889ce26-a22d-4730-9ea1-312c65be3487"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.scatterplot(data=df_train[::100], x = 'src_bytes', y = 'dst_bytes', hue='is_outlier', style='attack_class')\n",
        "plt.xscale('log')\n",
        "plt.yscale('log')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "5e41f01b-2f03-41af-9398-cb6692589fba",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5e41f01b-2f03-41af-9398-cb6692589fba",
        "outputId": "3c930427-4c30-4265-8ae9-8eed13326c8e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "skewed_cols = ['duration','src_bytes','dst_bytes']\n",
        "\n",
        "def apply_log_transform(df, cols):\n",
        "    df_copy = df.copy()\n",
        "\n",
        "    # Apply log1p to all selected columns at once\n",
        "    df_copy[[f\"log_{col}\" for col in cols]] = np.log1p(df_copy[cols])\n",
        "\n",
        "    # Plot comparison\n",
        "    for col in cols:\n",
        "        plt.figure(figsize=(10,4))\n",
        "\n",
        "        plt.subplot(1,2,1)\n",
        "        sns.histplot(df[col], bins=30, kde=True)\n",
        "        plt.title(f\"Original {col}\")\n",
        "\n",
        "        plt.subplot(1,2,2)\n",
        "        sns.histplot(df_copy[f\"log_{col}\"], bins=30, kde=True)\n",
        "        plt.title(f\"Log Transformed {col}\")\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    return df_copy\n",
        "\n",
        "\n",
        "df_train = apply_log_transform(df_train, skewed_cols)\n",
        "df_test = apply_log_transform(df_test, skewed_cols)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "3953d962-a3e3-4a45-a762-0b610428fb89",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3953d962-a3e3-4a45-a762-0b610428fb89",
        "outputId": "21dbe244-649a-4b15-f073-68c091c68842"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New Feature Set Head:\n",
            "   src_bytes_ratio  packet_rate  is_error_flag  same_srv_interaction\n",
            "0         1.000000     200000.0              0                 0.170\n",
            "1         1.000000    1300000.0              0                 0.000\n",
            "2         0.000000   12300000.0              1                 0.005\n",
            "3         0.027668     500000.0              0                 1.000\n",
            "4         0.321486    3000000.0              0                 1.000\n"
          ]
        }
      ],
      "source": [
        "def engineer_features(df):\n",
        "    df_eng = df.copy()\n",
        "\n",
        "    # 1. Total Bytes\n",
        "    df_eng['total_bytes'] = df_eng['src_bytes'] + df_eng['dst_bytes']\n",
        "\n",
        "    # 2. Byte Ratio (Handle division by zero)\n",
        "    df_eng['src_bytes_ratio'] = df_eng['src_bytes'] / (df_eng['total_bytes'] + 1e-5)\n",
        "\n",
        "    # 3. Load / Packet Rate\n",
        "    # If duration is 0, we assume it's a single instantaneous event (rate = count)\n",
        "    df_eng['packet_rate'] = df_eng['count'] / (df_eng['duration'] + 1e-5)\n",
        "\n",
        "    # 4. Title: Flow Asymmetry\n",
        "    df_eng['byte_diff'] = abs(df_eng['src_bytes'] - df_eng['dst_bytes'])\n",
        "\n",
        "    # 5. Flag interactions (example: is it an error flag?)\n",
        "    error_flags = ['S0', 'S1', 'S2', 'S3', 'REJ']\n",
        "    df_eng['is_error_flag'] = df_eng['flag'].isin(error_flags).astype(int)\n",
        "\n",
        "    # 6. Critical Host Interaction\n",
        "    # Interaction between 'same_srv_rate' and 'dst_host_same_srv_rate'\n",
        "    df_eng['same_srv_interaction'] = df_eng['same_srv_rate'] * df_eng['dst_host_same_srv_rate']\n",
        "\n",
        "    return df_eng\n",
        "\n",
        "df_train = engineer_features(df_train)\n",
        "df_test = engineer_features(df_test)\n",
        "\n",
        "print(\"New Feature Set Head:\")\n",
        "print(df_train[['src_bytes_ratio', 'packet_rate', 'is_error_flag', 'same_srv_interaction']].head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "d257349b-91c3-4600-99bb-e60122dccd13",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d257349b-91c3-4600-99bb-e60122dccd13",
        "outputId": "3fce0bcf-965e-45fe-e7ca-fab4d949a2a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New Feature Set Head:\n",
            "   src_bytes_ratio  packet_rate  is_error_flag  same_srv_interaction\n",
            "0         1.000000     200000.0              0                 0.170\n",
            "1         1.000000    1300000.0              0                 0.000\n",
            "2         0.000000   12300000.0              1                 0.005\n",
            "3         0.027668     500000.0              0                 1.000\n",
            "4         0.321486    3000000.0              0                 1.000\n"
          ]
        }
      ],
      "source": [
        "def engineer_features(df):\n",
        "    return df.assign(\n",
        "        total_bytes = df['src_bytes'] + df['dst_bytes'],\n",
        "        src_bytes_ratio = df['src_bytes'] / (df['src_bytes'] + df['dst_bytes'] + 1e-5),\n",
        "        packet_rate = df['count'] / (df['duration'] + 1e-5),\n",
        "        byte_diff = abs(df['src_bytes'] - df['dst_bytes']),\n",
        "        is_error_flag = df['flag'].isin(['S0','S1','S2','S3','REJ']).astype(int),\n",
        "        same_srv_interaction = df['same_srv_rate'] * df['dst_host_same_srv_rate']\n",
        "    )\n",
        "\n",
        "df_train = engineer_features(df_train)\n",
        "df_test = engineer_features(df_test)\n",
        "\n",
        "print(\"New Feature Set Head:\")\n",
        "print(df_train[['src_bytes_ratio', 'packet_rate', 'is_error_flag', 'same_srv_interaction']].head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "c5c8b810-120f-4531-9e77-fd5cc9debdf1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "c5c8b810-120f-4531-9e77-fd5cc9debdf1",
        "outputId": "a7818be7-c4df-485a-dbec-63e9002df6f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         ftp_data\n",
              "1            other\n",
              "2          private\n",
              "3             http\n",
              "4             http\n",
              "            ...   \n",
              "125968     private\n",
              "125969     private\n",
              "125970        smtp\n",
              "125971      klogin\n",
              "125972    ftp_data\n",
              "Name: service, Length: 125973, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>service</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ftp_data</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>http</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>http</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125968</th>\n",
              "      <td>private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125969</th>\n",
              "      <td>private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>ftp_data</td>\n",
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              "<p>125973 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "df_train['service']"
      ]
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    {
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      "id": "fb98414e-5f12-4387-8add-da05bc7643be",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_train_encode');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train_encode"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "cols_to_encode = ['protocol_type', 'flag']\n",
        "\n",
        "ohe = OneHotEncoder(sparse_output=False, handle_unknown='ignore')\n",
        "\n",
        "# Fit on train\n",
        "ohe.fit(df_train[cols_to_encode])\n",
        "\n",
        "# Transform train & test directly\n",
        "train_encoded = pd.DataFrame(\n",
        "    ohe.transform(df_train[cols_to_encode]),\n",
        "    columns=ohe.get_feature_names_out(cols_to_encode),\n",
        "    index=df_train.index\n",
        ")\n",
        "\n",
        "test_encoded = pd.DataFrame(\n",
        "    ohe.transform(df_test[cols_to_encode]),\n",
        "    columns=ohe.get_feature_names_out(cols_to_encode),\n",
        "    index=df_test.index\n",
        ")\n",
        "\n",
        "# Frequency encoding\n",
        "freq_map = df_train['service'].value_counts(normalize=True)\n",
        "\n",
        "df_train['service_freq'] = df_train['service'].map(freq_map)\n",
        "df_test['service_freq'] = df_test['service'].map(freq_map).fillna(0)\n",
        "\n",
        "# Final datasets\n",
        "df_train_encode = pd.concat([df_train, train_encoded], axis=1)\n",
        "df_test_encode = pd.concat([df_test, test_encoded], axis=1)\n",
        "df_train_encode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "6154d958-6aee-4c15-b1e9-8252e6afd9ed",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6154d958-6aee-4c15-b1e9-8252e6afd9ed",
        "outputId": "be2ce3a4-7e8d-4966-b1ee-30595d4b1441"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['duration', 'protocol_type', 'service', 'flag', 'src_bytes',\n",
            "       'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot',\n",
            "       'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell',\n",
            "       'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n",
            "       'num_access_files', 'num_outbound_cmds', 'is_host_login',\n",
            "       'is_guest_login', 'count', 'srv_count', 'serror_rate',\n",
            "       'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate',\n",
            "       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',\n",
            "       'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
            "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
            "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
            "       'dst_host_srv_serror_rate', 'dst_host_rerror_rate',\n",
            "       'dst_host_srv_rerror_rate', 'label', 'difficulty_level', 'is_outlier',\n",
            "       'attack_class', 'log_duration', 'log_src_bytes', 'log_dst_bytes',\n",
            "       'total_bytes', 'src_bytes_ratio', 'packet_rate', 'byte_diff',\n",
            "       'is_error_flag', 'same_srv_interaction', 'service_freq',\n",
            "       'protocol_type_icmp', 'protocol_type_tcp', 'protocol_type_udp',\n",
            "       'flag_OTH', 'flag_REJ', 'flag_RSTO', 'flag_RSTOS0', 'flag_RSTR',\n",
            "       'flag_S0', 'flag_S1', 'flag_S2', 'flag_S3', 'flag_SF', 'flag_SH'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(df_train_encode.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "id": "74897b77-08a4-440a-bc66-670717df71fb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74897b77-08a4-440a-bc66-670717df71fb",
        "outputId": "9cc0c706-6b3d-4af0-bc6f-08b8903715a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['duration', 'protocol_type', 'service', 'flag', 'src_bytes',\n",
            "       'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot',\n",
            "       'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell',\n",
            "       'su_attempted', 'num_root', 'num_file_creations', 'num_shells',\n",
            "       'num_access_files', 'num_outbound_cmds', 'is_host_login',\n",
            "       'is_guest_login', 'count', 'srv_count', 'serror_rate',\n",
            "       'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate',\n",
            "       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',\n",
            "       'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
            "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
            "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
            "       'dst_host_srv_serror_rate', 'dst_host_rerror_rate',\n",
            "       'dst_host_srv_rerror_rate', 'label', 'difficulty_level', 'attack_class',\n",
            "       'log_duration', 'log_src_bytes', 'log_dst_bytes', 'total_bytes',\n",
            "       'src_bytes_ratio', 'packet_rate', 'byte_diff', 'is_error_flag',\n",
            "       'same_srv_interaction', 'service_freq', 'protocol_type_icmp',\n",
            "       'protocol_type_tcp', 'protocol_type_udp', 'flag_OTH', 'flag_REJ',\n",
            "       'flag_RSTO', 'flag_RSTOS0', 'flag_RSTR', 'flag_S0', 'flag_S1',\n",
            "       'flag_S2', 'flag_S3', 'flag_SF', 'flag_SH'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(df_test_encode.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "2387585c-7466-4c0d-9d5f-6a7effeb401f",
      "metadata": {
        "id": "2387585c-7466-4c0d-9d5f-6a7effeb401f"
      },
      "outputs": [],
      "source": [
        "df_train_encode.drop(columns=['protocol_type','flag','service'],inplace = True)\n",
        "df_test_encode.drop(columns=['protocol_type','flag','service'],inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "c53b2abb-8912-4692-976a-01e19dcacb5e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "c53b2abb-8912-4692-976a-01e19dcacb5e",
        "outputId": "9ba94666-de8b-4bf5-8211-24ce13ef0502"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(125973, 66)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       duration  src_bytes  dst_bytes  land  wrong_fragment  urgent  hot  \\\n",
              "0             0          0          0     0               0       0    0   \n",
              "1             0          0          0     0               0       0    0   \n",
              "2             2      12983          0     0               0       0    0   \n",
              "3             0         20          0     0               0       0    0   \n",
              "4             1          0         15     0               0       0    0   \n",
              "...         ...        ...        ...   ...             ...     ...  ...   \n",
              "22539         0        794        333     0               0       0    0   \n",
              "22540         0        317        938     0               0       0    0   \n",
              "22541         0      54540       8314     0               0       0    2   \n",
              "22542         0         42         42     0               0       0    0   \n",
              "22543         0          0          0     0               0       0    0   \n",
              "\n",
              "       num_failed_logins  logged_in  num_compromised  ...  flag_REJ  \\\n",
              "0                      0          0                0  ...       1.0   \n",
              "1                      0          0                0  ...       1.0   \n",
              "2                      0          0                0  ...       0.0   \n",
              "3                      0          0                0  ...       0.0   \n",
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              "22542                  0          0                0  ...       0.0   \n",
              "22543                  0          0                0  ...       1.0   \n",
              "\n",
              "       flag_RSTO  flag_RSTOS0  flag_RSTR  flag_S0  flag_S1  flag_S2  flag_S3  \\\n",
              "0            0.0          0.0        0.0      0.0      0.0      0.0      0.0   \n",
              "1            0.0          0.0        0.0      0.0      0.0      0.0      0.0   \n",
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              "4            1.0          0.0        0.0      0.0      0.0      0.0      0.0   \n",
              "...          ...          ...        ...      ...      ...      ...      ...   \n",
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              "22543        0.0          0.0        0.0      0.0      0.0      0.0      0.0   \n",
              "\n",
              "       flag_SF  flag_SH  \n",
              "0          0.0      0.0  \n",
              "1          0.0      0.0  \n",
              "2          1.0      0.0  \n",
              "3          1.0      0.0  \n",
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              "...        ...      ...  \n",
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              "\n",
              "[22544 rows x 65 columns]"
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22542</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22543</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22544 rows × 65 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a254447-fb0c-4362-83fc-08e3acd1777d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6a254447-fb0c-4362-83fc-08e3acd1777d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6a254447-fb0c-4362-83fc-08e3acd1777d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_4fa31cfb-d778-4b12-bbbd-bfe33374bad9\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_test_encode')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_4fa31cfb-d778-4b12-bbbd-bfe33374bad9 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_test_encode');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test_encode"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "print(df_train_encode.shape)\n",
        "\n",
        "df_test_encode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "96ab7745-e81e-4868-9bbc-477637d8c5c0",
      "metadata": {
        "id": "96ab7745-e81e-4868-9bbc-477637d8c5c0"
      },
      "outputs": [],
      "source": [
        "#to see col are numeric\n",
        "df_numeric = df_train_encode.select_dtypes(include=['int64','float64'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "id": "e5e83ec7-66ea-4582-ad6c-a9ea5e7abaee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5e83ec7-66ea-4582-ad6c-a9ea5e7abaee",
        "outputId": "12aa1fc3-f5a3-4457-f21c-abcf72a760da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(125973, 64)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "print(df_numeric.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "2ed18815-c1af-4224-b6c5-e2b1a2f6855d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "2ed18815-c1af-4224-b6c5-e2b1a2f6855d",
        "outputId": "506a3b75-4f0a-45d0-bd1f-6752f02083c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "duration            int64\n",
              "src_bytes           int64\n",
              "dst_bytes           int64\n",
              "land                int64\n",
              "wrong_fragment      int64\n",
              "                   ...   \n",
              "flag_S1           float64\n",
              "flag_S2           float64\n",
              "flag_S3           float64\n",
              "flag_SF           float64\n",
              "flag_SH           float64\n",
              "Length: 66, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>duration</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>src_bytes</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dst_bytes</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>land</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrong_fragment</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_S1</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_S2</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_S3</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_SF</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_SH</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "#to check data types\n",
        "df_train_encode.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "id": "3fbbff8a-c4a4-4c5b-8bf0-a6cf2c780190",
      "metadata": {
        "id": "3fbbff8a-c4a4-4c5b-8bf0-a6cf2c780190"
      },
      "outputs": [],
      "source": [
        "df_numeric = df_train_encode.select_dtypes(include=np.number)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "8208bb4a-51d7-436d-9e6b-94c8ae3911df",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8208bb4a-51d7-436d-9e6b-94c8ae3911df",
        "outputId": "682618e3-e88f-400e-b07d-ed3d68927049"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numeric features count: 64\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "df_numeric = df_train_encode.select_dtypes(include=np.number)\n",
        "\n",
        "print(\"Numeric features count:\", df_numeric.shape[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "146c0d0d-ca8c-427e-b422-13dabe29cfd0",
      "metadata": {
        "id": "146c0d0d-ca8c-427e-b422-13dabe29cfd0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "corr_matrix = df_numeric.corr().abs()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "0182a859-3df7-4d8c-a35a-583bf8d1f554",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "0182a859-3df7-4d8c-a35a-583bf8d1f554",
        "outputId": "f77f8b8a-70cf-42a5-ecee-2dfe5f7cef31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                duration  src_bytes  dst_bytes      land  wrong_fragment  \\\n",
              "duration        1.000000   0.070737   0.034878 -0.001553       -0.009866   \n",
              "src_bytes       0.070737   1.000000   0.000204 -0.000109       -0.000693   \n",
              "dst_bytes       0.034878   0.000204   1.000000 -0.000069       -0.000440   \n",
              "land           -0.001553  -0.000109  -0.000069  1.000000       -0.001261   \n",
              "wrong_fragment -0.009866  -0.000693  -0.000440 -0.001261        1.000000   \n",
              "...                  ...        ...        ...       ...             ...   \n",
              "flag_S1        -0.005943   0.000007   0.000147 -0.000759       -0.004824   \n",
              "flag_S2        -0.003411  -0.000229   0.000215 -0.000448       -0.002843   \n",
              "flag_S3         0.001148   0.000682  -0.000077 -0.000278       -0.001765   \n",
              "flag_SF        -0.068255  -0.006079  -0.004509 -0.017074        0.073840   \n",
              "flag_SH        -0.005119  -0.000360  -0.000228 -0.000654       -0.004155   \n",
              "\n",
              "                  urgent       hot  num_failed_logins  logged_in  \\\n",
              "duration        0.003830  0.000705           0.009528  -0.064218   \n",
              "src_bytes      -0.000059  0.000295          -0.000208  -0.003353   \n",
              "dst_bytes       0.000248 -0.000344           0.000504  -0.002894   \n",
              "land           -0.000109 -0.001340          -0.000381  -0.011402   \n",
              "wrong_fragment -0.000692 -0.008508          -0.002418  -0.072418   \n",
              "...                  ...       ...                ...        ...   \n",
              "flag_S1        -0.000417 -0.000797           0.001808   0.052719   \n",
              "flag_S2        -0.000246  0.008963          -0.000858   0.033628   \n",
              "flag_S3        -0.000153 -0.000190           0.017265   0.019436   \n",
              "flag_SF         0.006383  0.074924           0.000136   0.641738   \n",
              "flag_SH        -0.000359 -0.004255          -0.001255  -0.037575   \n",
              "\n",
              "                num_compromised  ...  flag_REJ  flag_RSTO  flag_RSTOS0  \\\n",
              "duration               0.042679  ... -0.034493  -0.005336     0.034268   \n",
              "src_bytes             -0.000086  ... -0.002429   0.007609     0.178046   \n",
              "dst_bytes              0.001233  ... -0.001539  -0.000537    -0.000141   \n",
              "land                  -0.000164  ... -0.004408  -0.001579    -0.000403   \n",
              "wrong_fragment        -0.001044  ... -0.027999  -0.010027    -0.002560   \n",
              "...                         ...  ...       ...        ...          ...   \n",
              "flag_S1                0.016717  ... -0.016867  -0.006040    -0.001542   \n",
              "flag_S2               -0.000360  ... -0.009940  -0.003560    -0.000909   \n",
              "flag_S3               -0.000129  ... -0.006172  -0.002210    -0.000564   \n",
              "flag_SF                0.007291  ... -0.379191  -0.135793    -0.034668   \n",
              "flag_SH               -0.000542  ... -0.014528  -0.005203    -0.001328   \n",
              "\n",
              "                flag_RSTR   flag_S0   flag_S1   flag_S2   flag_S3   flag_SF  \\\n",
              "duration         0.539954 -0.068182 -0.005943 -0.003411  0.001148 -0.068255   \n",
              "src_bytes       -0.000685 -0.004800  0.000007 -0.000229  0.000682 -0.006079   \n",
              "dst_bytes        0.029685 -0.003042  0.000147  0.000215 -0.000077 -0.004509   \n",
              "land            -0.001972  0.022781 -0.000759 -0.000448 -0.000278 -0.017074   \n",
              "wrong_fragment  -0.012526 -0.055342 -0.004824 -0.002843 -0.001765  0.073840   \n",
              "...                   ...       ...       ...       ...       ...       ...   \n",
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              "flag_S2         -0.004447 -0.019646 -0.001712  1.000000 -0.000627 -0.038499   \n",
              "flag_S3         -0.002761 -0.012199 -0.001063 -0.000627  1.000000 -0.023906   \n",
              "flag_SF         -0.169644 -0.749485 -0.065329 -0.038499 -0.023906  1.000000   \n",
              "flag_SH         -0.006500 -0.028715 -0.002503 -0.001475 -0.000916 -0.056270   \n",
              "\n",
              "                 flag_SH  \n",
              "duration       -0.005119  \n",
              "src_bytes      -0.000360  \n",
              "dst_bytes      -0.000228  \n",
              "land           -0.000654  \n",
              "wrong_fragment -0.004155  \n",
              "...                  ...  \n",
              "flag_S1        -0.002503  \n",
              "flag_S2        -0.001475  \n",
              "flag_S3        -0.000916  \n",
              "flag_SF        -0.056270  \n",
              "flag_SH         1.000000  \n",
              "\n",
              "[64 rows x 64 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b709ab4-2a49-4cf9-bd45-d316a89009b0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>...</th>\n",
              "      <th>flag_REJ</th>\n",
              "      <th>flag_RSTO</th>\n",
              "      <th>flag_RSTOS0</th>\n",
              "      <th>flag_RSTR</th>\n",
              "      <th>flag_S0</th>\n",
              "      <th>flag_S1</th>\n",
              "      <th>flag_S2</th>\n",
              "      <th>flag_S3</th>\n",
              "      <th>flag_SF</th>\n",
              "      <th>flag_SH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>duration</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.070737</td>\n",
              "      <td>0.034878</td>\n",
              "      <td>-0.001553</td>\n",
              "      <td>-0.009866</td>\n",
              "      <td>0.003830</td>\n",
              "      <td>0.000705</td>\n",
              "      <td>0.009528</td>\n",
              "      <td>-0.064218</td>\n",
              "      <td>0.042679</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.034493</td>\n",
              "      <td>-0.005336</td>\n",
              "      <td>0.034268</td>\n",
              "      <td>0.539954</td>\n",
              "      <td>-0.068182</td>\n",
              "      <td>-0.005943</td>\n",
              "      <td>-0.003411</td>\n",
              "      <td>0.001148</td>\n",
              "      <td>-0.068255</td>\n",
              "      <td>-0.005119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>src_bytes</th>\n",
              "      <td>0.070737</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000204</td>\n",
              "      <td>-0.000109</td>\n",
              "      <td>-0.000693</td>\n",
              "      <td>-0.000059</td>\n",
              "      <td>0.000295</td>\n",
              "      <td>-0.000208</td>\n",
              "      <td>-0.003353</td>\n",
              "      <td>-0.000086</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.002429</td>\n",
              "      <td>0.007609</td>\n",
              "      <td>0.178046</td>\n",
              "      <td>-0.000685</td>\n",
              "      <td>-0.004800</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>-0.000229</td>\n",
              "      <td>0.000682</td>\n",
              "      <td>-0.006079</td>\n",
              "      <td>-0.000360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dst_bytes</th>\n",
              "      <td>0.034878</td>\n",
              "      <td>0.000204</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000069</td>\n",
              "      <td>-0.000440</td>\n",
              "      <td>0.000248</td>\n",
              "      <td>-0.000344</td>\n",
              "      <td>0.000504</td>\n",
              "      <td>-0.002894</td>\n",
              "      <td>0.001233</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.001539</td>\n",
              "      <td>-0.000537</td>\n",
              "      <td>-0.000141</td>\n",
              "      <td>0.029685</td>\n",
              "      <td>-0.003042</td>\n",
              "      <td>0.000147</td>\n",
              "      <td>0.000215</td>\n",
              "      <td>-0.000077</td>\n",
              "      <td>-0.004509</td>\n",
              "      <td>-0.000228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>land</th>\n",
              "      <td>-0.001553</td>\n",
              "      <td>-0.000109</td>\n",
              "      <td>-0.000069</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001261</td>\n",
              "      <td>-0.000109</td>\n",
              "      <td>-0.001340</td>\n",
              "      <td>-0.000381</td>\n",
              "      <td>-0.011402</td>\n",
              "      <td>-0.000164</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.004408</td>\n",
              "      <td>-0.001579</td>\n",
              "      <td>-0.000403</td>\n",
              "      <td>-0.001972</td>\n",
              "      <td>0.022781</td>\n",
              "      <td>-0.000759</td>\n",
              "      <td>-0.000448</td>\n",
              "      <td>-0.000278</td>\n",
              "      <td>-0.017074</td>\n",
              "      <td>-0.000654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrong_fragment</th>\n",
              "      <td>-0.009866</td>\n",
              "      <td>-0.000693</td>\n",
              "      <td>-0.000440</td>\n",
              "      <td>-0.001261</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000692</td>\n",
              "      <td>-0.008508</td>\n",
              "      <td>-0.002418</td>\n",
              "      <td>-0.072418</td>\n",
              "      <td>-0.001044</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.027999</td>\n",
              "      <td>-0.010027</td>\n",
              "      <td>-0.002560</td>\n",
              "      <td>-0.012526</td>\n",
              "      <td>-0.055342</td>\n",
              "      <td>-0.004824</td>\n",
              "      <td>-0.002843</td>\n",
              "      <td>-0.001765</td>\n",
              "      <td>0.073840</td>\n",
              "      <td>-0.004155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_S1</th>\n",
              "      <td>-0.005943</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000147</td>\n",
              "      <td>-0.000759</td>\n",
              "      <td>-0.004824</td>\n",
              "      <td>-0.000417</td>\n",
              "      <td>-0.000797</td>\n",
              "      <td>0.001808</td>\n",
              "      <td>0.052719</td>\n",
              "      <td>0.016717</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.016867</td>\n",
              "      <td>-0.006040</td>\n",
              "      <td>-0.001542</td>\n",
              "      <td>-0.007546</td>\n",
              "      <td>-0.033338</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001712</td>\n",
              "      <td>-0.001063</td>\n",
              "      <td>-0.065329</td>\n",
              "      <td>-0.002503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_S2</th>\n",
              "      <td>-0.003411</td>\n",
              "      <td>-0.000229</td>\n",
              "      <td>0.000215</td>\n",
              "      <td>-0.000448</td>\n",
              "      <td>-0.002843</td>\n",
              "      <td>-0.000246</td>\n",
              "      <td>0.008963</td>\n",
              "      <td>-0.000858</td>\n",
              "      <td>0.033628</td>\n",
              "      <td>-0.000360</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>-0.003560</td>\n",
              "      <td>-0.000909</td>\n",
              "      <td>-0.004447</td>\n",
              "      <td>-0.019646</td>\n",
              "      <td>-0.001712</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000627</td>\n",
              "      <td>-0.038499</td>\n",
              "      <td>-0.001475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_S3</th>\n",
              "      <td>0.001148</td>\n",
              "      <td>0.000682</td>\n",
              "      <td>-0.000077</td>\n",
              "      <td>-0.000278</td>\n",
              "      <td>-0.001765</td>\n",
              "      <td>-0.000153</td>\n",
              "      <td>-0.000190</td>\n",
              "      <td>0.017265</td>\n",
              "      <td>0.019436</td>\n",
              "      <td>-0.000129</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.006172</td>\n",
              "      <td>-0.002210</td>\n",
              "      <td>-0.000564</td>\n",
              "      <td>-0.002761</td>\n",
              "      <td>-0.012199</td>\n",
              "      <td>-0.001063</td>\n",
              "      <td>-0.000627</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.023906</td>\n",
              "      <td>-0.000916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_SF</th>\n",
              "      <td>-0.068255</td>\n",
              "      <td>-0.006079</td>\n",
              "      <td>-0.004509</td>\n",
              "      <td>-0.017074</td>\n",
              "      <td>0.073840</td>\n",
              "      <td>0.006383</td>\n",
              "      <td>0.074924</td>\n",
              "      <td>0.000136</td>\n",
              "      <td>0.641738</td>\n",
              "      <td>0.007291</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.379191</td>\n",
              "      <td>-0.135793</td>\n",
              "      <td>-0.034668</td>\n",
              "      <td>-0.169644</td>\n",
              "      <td>-0.749485</td>\n",
              "      <td>-0.065329</td>\n",
              "      <td>-0.038499</td>\n",
              "      <td>-0.023906</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.056270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag_SH</th>\n",
              "      <td>-0.005119</td>\n",
              "      <td>-0.000360</td>\n",
              "      <td>-0.000228</td>\n",
              "      <td>-0.000654</td>\n",
              "      <td>-0.004155</td>\n",
              "      <td>-0.000359</td>\n",
              "      <td>-0.004255</td>\n",
              "      <td>-0.001255</td>\n",
              "      <td>-0.037575</td>\n",
              "      <td>-0.000542</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.014528</td>\n",
              "      <td>-0.005203</td>\n",
              "      <td>-0.001328</td>\n",
              "      <td>-0.006500</td>\n",
              "      <td>-0.028715</td>\n",
              "      <td>-0.002503</td>\n",
              "      <td>-0.001475</td>\n",
              "      <td>-0.000916</td>\n",
              "      <td>-0.056270</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>64 rows × 64 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b709ab4-2a49-4cf9-bd45-d316a89009b0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6b709ab4-2a49-4cf9-bd45-d316a89009b0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_3e85f376-16bb-4d2b-9c19-c01fd989dd02\">\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('corr_matrix')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3e85f376-16bb-4d2b-9c19-c01fd989dd02 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('corr_matrix');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "corr_matrix"
            }
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "corr_matrix = df_numeric.corr()\n",
        "corr_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "ea834c1b-1118-4095-9f65-27aceb342236",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "ea834c1b-1118-4095-9f65-27aceb342236",
        "outputId": "d3d769b5-35d3-4077-879c-8d14cdedc30d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Select numeric columns\n",
        "df_numeric = df_train_encode.select_dtypes(include=np.number)\n",
        "\n",
        "# Compute correlation\n",
        "corr_matrix = df_numeric.corr()\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(corr_matrix, cmap='coolwarm', center=0)\n",
        "plt.title(\"Correlation Matrix\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "483fe7a7-e11e-45e6-bcf5-b8b89f919412",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "483fe7a7-e11e-45e6-bcf5-b8b89f919412",
        "outputId": "4a60b1ec-e62c-490f-faf0-67d0e1c05de4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#strong correlation\n",
        "corr_matrix = df_numeric.corr().abs()\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(corr_matrix > 0.8, cmap='coolwarm')\n",
        "plt.title(\"Highly Correlated Features (> 0.8)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "42b5c933-c015-46cf-9686-6c97b8c0441d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42b5c933-c015-46cf-9686-6c97b8c0441d",
        "outputId": "9cdeb217-3e84-4266-bc56-bcef586934a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highly correlated features: ['num_root', 'srv_serror_rate', 'srv_rerror_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'packet_rate', 'byte_diff', 'same_srv_interaction', 'flag_S0', 'flag_SF']\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "df_numeric = df_train_encode.select_dtypes(include=np.number)\n",
        "\n",
        "corr_matrix = df_numeric.corr().abs()\n",
        "\n",
        "upper = corr_matrix.where(\n",
        "    np.triu(np.ones(corr_matrix.shape), k=1).astype(bool)\n",
        ")\n",
        "\n",
        "to_drop_corr = [col for col in upper.columns if any(upper[col] > 0.9)]\n",
        "\n",
        "print(\"Highly correlated features:\", to_drop_corr)\n",
        "\n",
        "df_train_clean = df_numeric.drop(columns=to_drop_corr)\n",
        "df_test_clean = df_test_encode.drop(columns=to_drop_corr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "id": "267ac405-5f5b-4966-911e-c940d1ae8ec7",
      "metadata": {
        "id": "267ac405-5f5b-4966-911e-c940d1ae8ec7"
      },
      "outputs": [],
      "source": [
        "# Ensure same columns before variance selection\n",
        "#df_test_clean = df_test_clean.reindex(columns=df_train_clean.columns, fill_value=0)\n",
        "\n",
        "#selector = VarianceThreshold(threshold=0.01)\n",
        "#selector.fit(df_train_clean)\n",
        "\n",
        "#selected_cols = df_train_clean.columns[selector.get_support()]\n",
        "\n",
        "#df_train_clean = df_train_clean[selected_cols]\n",
        "#df_test_clean = df_test_clean[selected_cols]\n",
        "\n",
        "#print(\"Remaining features:\", len(selected_cols))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "id": "e2aa4722-ffd6-4d3d-bac4-7540e203e553",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2aa4722-ffd6-4d3d-bac4-7540e203e553",
        "outputId": "95533f6f-987e-4d65-e47e-f0d45fd52fcc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highly correlated features: ['num_root', 'srv_serror_rate', 'srv_rerror_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_srv_rerror_rate', 'packet_rate', 'byte_diff', 'same_srv_interaction', 'flag_S0']\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "df_numeric = df_train_encode.select_dtypes(include=np.number)\n",
        "\n",
        "corr_matrix = df_numeric.corr().abs()\n",
        "\n",
        "upper = corr_matrix.where(\n",
        "    np.triu(np.ones(corr_matrix.shape), k=1).astype(bool)\n",
        ")\n",
        "\n",
        "to_drop_corr = [col for col in upper.columns if any(upper[col] > 0.95)]\n",
        "\n",
        "print(\"Highly correlated features:\", to_drop_corr)\n",
        "\n",
        "df_train_clean = df_numeric.drop(columns=to_drop_corr)\n",
        "df_test_clean = df_test_encode.drop(columns=to_drop_corr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "id": "e75015b1-51e8-48d9-ae37-fa9ea9ccd4c3",
      "metadata": {
        "id": "e75015b1-51e8-48d9-ae37-fa9ea9ccd4c3"
      },
      "outputs": [],
      "source": [
        "# Ensure same columns before variance selection\n",
        "#df_test_clean = df_test_clean.reindex(columns=df_train_clean.columns, fill_value=0)\n",
        "\n",
        "#selector = VarianceThreshold(threshold=0.01)\n",
        "#selector.fit(df_train_clean)\n",
        "\n",
        "#selected_cols = df_train_clean.columns[selector.get_support()]\n",
        "\n",
        "#df_train_clean = df_train_clean[selected_cols]\n",
        "#df_test_clean = df_test_clean[selected_cols]\n",
        "\n",
        "#print(\"Remaining features:\", len(selected_cols))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "id": "94mNCpNDIqsK",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94mNCpNDIqsK",
        "outputId": "f1d5821d-4634-427b-9f81-4d649f98ebaa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highly correlated features: ['num_root', 'srv_serror_rate', 'srv_rerror_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_srv_rerror_rate', 'packet_rate', 'byte_diff', 'same_srv_interaction', 'flag_S0']\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "df_numeric = df_train_encode.select_dtypes(include=np.number)\n",
        "\n",
        "corr_matrix = df_numeric.corr().abs()\n",
        "\n",
        "upper = corr_matrix.where(\n",
        "    np.triu(np.ones(corr_matrix.shape), k=1).astype(bool)\n",
        ")\n",
        "\n",
        "to_drop_corr = [col for col in upper.columns if any(upper[col] > 0.95)]\n",
        "\n",
        "print(\"Highly correlated features:\", to_drop_corr)\n",
        "\n",
        "df_train_clean = df_numeric.drop(columns=to_drop_corr)\n",
        "df_test_clean = df_test_encode.drop(columns=to_drop_corr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "id": "xPd0pqhFIwI3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPd0pqhFIwI3",
        "outputId": "80ee556c-8cda-4439-bfcb-7c9ed2d04924"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           duration     src_bytes     dst_bytes           land  wrong_fragment\n",
            "count  125973.00000  1.259730e+05  1.259730e+05  125973.000000   125973.000000\n",
            "mean      287.14465  1.649375e+02  3.833162e+01       0.000198        0.022687\n",
            "std      2604.51531  2.126932e+04  7.793157e+03       0.014086        0.253530\n",
            "min         0.00000 -1.594203e-01  0.000000e+00       0.000000        0.000000\n",
            "25%         0.00000 -1.594203e-01  0.000000e+00       0.000000        0.000000\n",
            "50%         0.00000  0.000000e+00  0.000000e+00       0.000000        0.000000\n",
            "75%         0.00000  8.405797e-01  1.000000e+00       0.000000        0.000000\n",
            "max     42908.00000  4.999869e+06  2.538638e+06       1.000000        3.000000\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "\n",
        "# Remove target + unwanted columns\n",
        "cols_to_remove = ['label', 'attack_class', 'difficulty_level', 'is_outlier']\n",
        "\n",
        "scale_features = [\n",
        "    col for col in df_train_encode.columns\n",
        "    if col not in cols_to_remove\n",
        "]\n",
        "\n",
        "# Apply RobustScaler\n",
        "scaler = RobustScaler()\n",
        "\n",
        "# Fit only on train\n",
        "df_train_encode[scale_features] = scaler.fit_transform(\n",
        "    df_train_encode[scale_features]\n",
        ")\n",
        "\n",
        "# Only transform test (NOT fit_transform)\n",
        "df_test_encode[scale_features] = scaler.transform(\n",
        "    df_test_encode[scale_features]\n",
        ")\n",
        "\n",
        "print(df_train_encode[scale_features[0:5]].describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "UIKgdtQwIypb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UIKgdtQwIypb",
        "outputId": "5d306d55-d3f4-49dd-b6ad-36d3dd5d45f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           duration     src_bytes     dst_bytes           land  wrong_fragment\n",
            "count  125973.00000  1.259730e+05  1.259730e+05  125973.000000   125973.000000\n",
            "mean      287.14465  1.649375e+02  3.833162e+01       0.000198        0.022687\n",
            "std      2604.51531  2.126932e+04  7.793157e+03       0.014086        0.253530\n",
            "min         0.00000 -1.594203e-01  0.000000e+00       0.000000        0.000000\n",
            "25%         0.00000 -1.594203e-01  0.000000e+00       0.000000        0.000000\n",
            "50%         0.00000  0.000000e+00  0.000000e+00       0.000000        0.000000\n",
            "75%         0.00000  8.405797e-01  1.000000e+00       0.000000        0.000000\n",
            "max     42908.00000  4.999869e+06  2.538638e+06       1.000000        3.000000\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "\n",
        "# Remove target + unwanted columns\n",
        "cols_to_remove = ['label', 'attack_class', 'difficulty_level', 'is_outlier']\n",
        "\n",
        "scale_features = [\n",
        "    col for col in df_train_encode.columns\n",
        "    if col not in cols_to_remove\n",
        "]\n",
        "\n",
        "# Apply RobustScaler\n",
        "scaler = RobustScaler()\n",
        "\n",
        "# Fit only on train\n",
        "df_train_encode[scale_features] = scaler.fit_transform(\n",
        "    df_train_encode[scale_features]\n",
        ")\n",
        "\n",
        "# Only transform test (NOT fit_transform)\n",
        "df_test_encode[scale_features] = scaler.transform(\n",
        "    df_test_encode[scale_features]\n",
        ")\n",
        "\n",
        "print(df_train_encode[scale_features[0:5]].describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "zRU9rc8rI0pG",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zRU9rc8rI0pG",
        "outputId": "9605657d-e545-4e21-b8fc-28194c39d65d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution BEFORE SMOTE:\n",
            "attack_class\n",
            "normal    67343\n",
            "DoS       45927\n",
            "Probe     11656\n",
            "R2L         995\n",
            "U2R          52\n",
            "Name: count, dtype: int64\n",
            "Class distribution AFTER SMOTE:\n",
            "attack_class\n",
            "normal    67343\n",
            "DoS       67343\n",
            "R2L       67343\n",
            "Probe     67343\n",
            "U2R       67343\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "import pandas as pd\n",
        "# Define Features & Target\n",
        "target_column = \"attack_class\"\n",
        "feature_columns = [\n",
        "    col for col in df_train_encode.columns\n",
        "    if col not in [\"label\", \"attack_class\", \"difficulty_level\"]\n",
        "]\n",
        "\n",
        "X = df_train_encode[feature_columns].copy()\n",
        "y = df_train_encode[target_column].copy()\n",
        "#  Handle Missing Values\n",
        "X = X.fillna(0)\n",
        "# Check Class Distribution Before SMOTE\n",
        "print(\"Class distribution BEFORE SMOTE:\")\n",
        "print(y.value_counts())\n",
        "# Apply SMOTE\n",
        "smote = SMOTE(\n",
        "    sampling_strategy=\"auto\",   # balances all minority classes\n",
        "    k_neighbors=5,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "X_resampled, y_resampled = smote.fit_resample(X, y)\n",
        "# Check Class Distribution After SMOTE\n",
        "print(\"Class distribution AFTER SMOTE:\")\n",
        "print(pd.Series(y_resampled).value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "xYnGjmv5I2tb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYnGjmv5I2tb",
        "outputId": "531220fd-edab-4816-94c2-c1c496425b93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.12/dist-packages (0.14.1)\n",
            "Requirement already satisfied: numpy<3,>=1.25.2 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (2.0.2)\n",
            "Requirement already satisfied: scipy<2,>=1.11.4 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (1.16.3)\n",
            "Requirement already satisfied: scikit-learn<2,>=1.4.2 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (1.6.1)\n",
            "Requirement already satisfied: sklearn-compat<0.2,>=0.1.5 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (0.1.5)\n",
            "Requirement already satisfied: joblib<2,>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (1.5.3)\n",
            "Requirement already satisfied: threadpoolctl<4,>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (3.6.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install imbalanced-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "U7BEAf9PI446",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "U7BEAf9PI446",
        "outputId": "cc337457-9dde-4e43-f2ab-521168146cca"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sample_idx = X_resampled.sample(5000, random_state=42).index\n",
        "X_sample = X_resampled.loc[sample_idx] #just for understanding\n",
        "y_sample = y_resampled.loc[sample_idx]\n",
        "# Apply PCA\n",
        "pca = PCA(n_components=2, random_state=42)\n",
        "x_pca = pca.fit_transform(X_sample)\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.scatterplot( x=x_pca[:,0], y=x_pca[:,1], hue=y_sample, alpha=0.6)\n",
        "plt.title(\"PCA Visualization (Post-SMOTE)\")\n",
        "plt.xlabel(\"Principal Component 1\")\n",
        "plt.ylabel(\"Principal Component 2\")\n",
        "plt.legend(title=\"Attack Class\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "SbT8bwflI7KI",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "SbT8bwflI7KI",
        "outputId": "2384438b-5278-4021-a4ad-7f7122489539"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#diff plots for clear understanding\n",
        "plt.figure(figsize=(6,6))\n",
        "\n",
        "for label in y_sample.unique():\n",
        "    plt.scatter(\n",
        "        x_pca[y_sample == label, 0],\n",
        "        x_pca[y_sample == label, 1],\n",
        "        label=label,\n",
        "        alpha=0.5\n",
        "    )\n",
        "\n",
        "plt.title(\"PCA - Class Wise Distribution\")\n",
        "plt.xlabel(\"PC1\")\n",
        "plt.ylabel(\"PC2\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "SoZ7VJz-I9it",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "SoZ7VJz-I9it",
        "outputId": "4fd43719-d6b3-4ed8-f60e-664f6a1c06c7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Before SMOTE\n",
        "sample_idx_orig = X.sample(5000, random_state=42).index\n",
        "X_orig_sample = X.loc[sample_idx_orig]\n",
        "y_orig_sample = y.loc[sample_idx_orig]\n",
        "\n",
        "pca_before = PCA(n_components=2, random_state=42)\n",
        "x_pca_before = pca_before.fit_transform(X_orig_sample)\n",
        "\n",
        "plt.figure(figsize=(6,6))\n",
        "sns.scatterplot(\n",
        "    x=x_pca_before[:,0],\n",
        "    y=x_pca_before[:,1],\n",
        "    hue=y_orig_sample,\n",
        "    alpha=0.6\n",
        ")\n",
        "\n",
        "plt.title(\"PCA Before SMOTE\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "id": "LTcB_foAI_ks",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "LTcB_foAI_ks",
        "outputId": "742a4317-13d2-4dec-935f-17352c2b3293"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "sns.kdeplot(\n",
        "    x=x_pca[:,0],\n",
        "    y=x_pca[:,1],\n",
        "    fill=True,\n",
        "    cmap=\"viridis\",\n",
        "    thresh=0.05\n",
        ")\n",
        "\n",
        "plt.title(\"Density Plot of PCA Components\")\n",
        "plt.xlabel(\"PC1\")\n",
        "plt.ylabel(\"PC2\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "jMRwQESiJByy",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMRwQESiJByy",
        "outputId": "507844ab-9766-4961-972e-eb0e6eb041cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved the Train Data to ../data/train_processed_advanced.csv\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "output_dir = \"../data/\"\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "# Combine TRAIN features + target\n",
        "df_final_train = pd.concat([X_resampled, y_resampled], axis=1)\n",
        "# Define save path\n",
        "train_save_path = os.path.join(output_dir, \"train_processed_advanced.csv\")\n",
        "\n",
        "# Save file\n",
        "df_final_train.to_csv(train_save_path, index=False)\n",
        "\n",
        "print(f\"Saved the Train Data to {train_save_path}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "id": "NafBkdHiJEYv",
      "metadata": {
        "id": "NafBkdHiJEYv"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "output_dir = \"../data/\"\n",
        "\n",
        "if not os.path.exists(output_dir):\n",
        "    os.makedirs(output_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "id": "6_7qtz7ZJGTd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_7qtz7ZJGTd",
        "outputId": "ca83137d-ca77-4781-ec73-4fee886bda97"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved the Train Data to ../data/test_processed_advanced.csv\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "output_dir = \"../data/\"\n",
        "os.makedirs(output_dir, exist_ok=True)\n",
        "# Combine TRAIN features + target\n",
        "df_final_test = pd.concat([X_resampled, y_resampled], axis=1)\n",
        "# Define save path\n",
        "test_save_path = os.path.join(output_dir, \"test_processed_advanced.csv\")\n",
        "\n",
        "# Save file\n",
        "df_final_test.to_csv(test_save_path, index=False)\n",
        "\n",
        "print(f\"Saved the Train Data to {test_save_path}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "zqITAY4CJjU1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqITAY4CJjU1",
        "outputId": "02fb8d84-cc9c-4c13-ec7d-24d628789a35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "attack_class\n",
            "normal    67343\n",
            "DoS       67343\n",
            "R2L       67343\n",
            "Probe     67343\n",
            "U2R       67343\n",
            "Name: count, dtype: int64\n",
            "attack_class\n",
            "normal    0.2\n",
            "DoS       0.2\n",
            "R2L       0.2\n",
            "Probe     0.2\n",
            "U2R       0.2\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(df_final_train['attack_class'].value_counts())\n",
        "print(df_final_train['attack_class'].value_counts(normalize=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "id": "tUXTtO9GJlY4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUXTtO9GJlY4",
        "outputId": "7a76b00e-f262-46ee-fd9f-c3c64b318830"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "attack_class\n",
            "normal    67343\n",
            "DoS       67343\n",
            "R2L       67343\n",
            "Probe     67343\n",
            "U2R       67343\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#actual samples for each class\n",
        "print(y_resampled.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "id": "CnpymYaiJnIo",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnpymYaiJnIo",
        "outputId": "502f0463-c805-4c00-c6cd-132d48277935"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "print(os.getcwd())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "id": "3md8v54JJpAM",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3md8v54JJpAM",
        "outputId": "1d5f2c3d-ffc7-4089-f15d-c3cd74a9ad08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['.config', 'data', 'derived_test_attack_category.csv', 'derived_train_attack_category.csv', 'sample_data']\n"
          ]
        }
      ],
      "source": [
        "print(os.listdir())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "id": "pfQu2oNeJrWV",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfQu2oNeJrWV",
        "outputId": "dd45884a-aced-4c9f-8628-9ff050e64395"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['KDDTest+.txt', 'KDDTrain+.txt']\n"
          ]
        }
      ],
      "source": [
        "print(os.listdir(\"data\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "id": "r1G7HimOJtAp",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1G7HimOJtAp",
        "outputId": "0bd2f908-956a-4373-a269-57b10c6b444f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current Working Directory:\n",
            "/content\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "print(\"Current Working Directory:\")\n",
        "print(os.getcwd())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "id": "pMOnBjihJuvj",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMOnBjihJuvj",
        "outputId": "9d8bbd76-e444-44cf-bf67-f981b8fed9a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['KDDTest+.txt', 'KDDTrain+.txt']\n",
            "Train shape: (336715, 64)\n",
            "Test shape: (336715, 64)\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "print(os.listdir(\"data\"))\n",
        "\n",
        "df_train = pd.read_csv(\"../data/train_processed_advanced.csv\")\n",
        "df_test = pd.read_csv(\"../data/test_processed_advanced.csv\")\n",
        "\n",
        "print(\"Train shape:\", df_train.shape)\n",
        "print(\"Test shape:\", df_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "id": "aD2lftsjJwYj",
      "metadata": {
        "id": "aD2lftsjJwYj"
      },
      "outputs": [],
      "source": [
        "#now we separate features and target\n",
        "X_train = df_train.drop(\"attack_class\", axis=1)\n",
        "y_train = df_train[\"attack_class\"]\n",
        "\n",
        "X_test = df_test.drop(\"attack_class\", axis=1)\n",
        "y_test = df_test[\"attack_class\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "id": "lcYZrHZ1JyOw",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcYZrHZ1JyOw",
        "outputId": "fe7dc8d3-db4d-49e2-ef60-bc0fa4670873"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model trained successfully!\n"
          ]
        }
      ],
      "source": [
        "#now we train KNN model\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "print(\"Model trained successfully!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "id": "CMQhdzoaJ0If",
      "metadata": {
        "id": "CMQhdzoaJ0If"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred = knn.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "id": "gBUTmGymJ2Td",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBUTmGymJ2Td",
        "outputId": "9245f7c2-49c6-4685-909b-a670176d491d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9966529557637764\n",
            "\n",
            "Confusion Matrix:\n",
            " [[67314     3     1     1    24]\n",
            " [    0 67273     4     0    66]\n",
            " [    0     1 67261    10    71]\n",
            " [    0     0     0 67343     0]\n",
            " [   29   594   207   116 66397]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         DoS       1.00      1.00      1.00     67343\n",
            "       Probe       0.99      1.00      1.00     67343\n",
            "         R2L       1.00      1.00      1.00     67343\n",
            "         U2R       1.00      1.00      1.00     67343\n",
            "      normal       1.00      0.99      0.99     67343\n",
            "\n",
            "    accuracy                           1.00    336715\n",
            "   macro avg       1.00      1.00      1.00    336715\n",
            "weighted avg       1.00      1.00      1.00    336715\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#now we will evaluate performance\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "id": "JYzYtFY4J4ex",
      "metadata": {
        "id": "JYzYtFY4J4ex"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "id": "XQWzLhyiJ7Gp",
      "metadata": {
        "id": "XQWzLhyiJ7Gp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "id": "7Jc1IX2bJ9PN",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Jc1IX2bJ9PN",
        "outputId": "1e75b2df-39dd-4342-a100-617567012f09"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classes: ['DoS' 'Probe' 'R2L' 'U2R' 'normal']\n"
          ]
        }
      ],
      "source": [
        "# y_resampled is your target\n",
        "le = LabelEncoder()\n",
        "y_encoded = le.fit_transform(y_resampled)\n",
        "\n",
        "print(\"Classes:\", le.classes_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "id": "3Qc_vwoEJ-5J",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "3Qc_vwoEJ-5J",
        "outputId": "29f590a6-5556-4a94-e286-a19bd1d75a68"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10, n_jobs=-1, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=10, n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "rf = RandomForestClassifier(\n",
        "    n_estimators=100,        # number of trees\n",
        "    random_state=42,         # reproducibility\n",
        "    max_depth=10,            # lightweight model\n",
        "    n_jobs=-1                # use all CPU cores\n",
        ")\n",
        "\n",
        "rf.fit(X_resampled, y_encoded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "id": "iusd46QtKAqa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iusd46QtKAqa",
        "outputId": "41a555f2-64f5-4fc6-b2ae-d5792a8c3d0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "service_freq                   0.058764\n",
            "src_bytes                      0.058514\n",
            "total_bytes                    0.058062\n",
            "dst_host_srv_count             0.051128\n",
            "logged_in                      0.048084\n",
            "byte_diff                      0.047203\n",
            "log_src_bytes                  0.045562\n",
            "dst_bytes                      0.044042\n",
            "log_dst_bytes                  0.039155\n",
            "root_shell                     0.037994\n",
            "srv_count                      0.032917\n",
            "dst_host_same_src_port_rate    0.032340\n",
            "src_bytes_ratio                0.031619\n",
            "dst_host_diff_srv_rate         0.030321\n",
            "dst_host_serror_rate           0.029620\n",
            "flag_S0                        0.028407\n",
            "num_file_creations             0.027755\n",
            "count                          0.024651\n",
            "packet_rate                    0.023749\n",
            "serror_rate                    0.022904\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "importances = rf.feature_importances_\n",
        "\n",
        "feature_importance = pd.Series(importances, index=X_resampled.columns)\n",
        "\n",
        "# Sort descending\n",
        "feature_importance = feature_importance.sort_values(ascending=False)\n",
        "\n",
        "print(feature_importance.head(20))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "id": "0aNp_MBRKCoz",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "0aNp_MBRKCoz",
        "outputId": "3dfb8532-1b0a-4338-d586-dedeb168ff07"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "top_features = feature_importance.head(25)\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "top_features.plot(kind='barh')\n",
        "plt.gca().invert_yaxis()\n",
        "plt.title(\"Top 25 Important Features\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "id": "wLE68fx9KEvp",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLE68fx9KEvp",
        "outputId": "23410101-11a0-42d5-ee9a-6d18b65e639e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of low importance features: 16\n",
            "flag_RSTR            0.000916\n",
            "flag_SH              0.000653\n",
            "flag_REJ             0.000630\n",
            "is_outlier           0.000445\n",
            "num_access_files     0.000398\n",
            "num_shells           0.000217\n",
            "urgent               0.000093\n",
            "su_attempted         0.000065\n",
            "flag_S1              0.000065\n",
            "flag_S3              0.000030\n",
            "flag_RSTOS0          0.000022\n",
            "land                 0.000008\n",
            "flag_S2              0.000005\n",
            "flag_OTH             0.000001\n",
            "is_host_login        0.000000\n",
            "num_outbound_cmds    0.000000\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "threshold = 0.001\n",
        "\n",
        "low_importance_features = feature_importance[feature_importance < threshold]\n",
        "\n",
        "print(\"Number of low importance features:\", len(low_importance_features))\n",
        "print(low_importance_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "id": "ewSp_mv5KGkR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewSp_mv5KGkR",
        "outputId": "a71c9e69-978d-446f-ca20-0a6d3cf74af9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New shape after feature selection: (336715, 47)\n"
          ]
        }
      ],
      "source": [
        "X_selected = X_resampled.drop(columns=low_importance_features.index)\n",
        "\n",
        "print(\"New shape after feature selection:\", X_selected.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.manifold import TSNE"
      ],
      "metadata": {
        "id": "pbINFP6Rv9Tp"
      },
      "id": "pbINFP6Rv9Tp",
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X_resampled)"
      ],
      "metadata": {
        "id": "l4Ud1BHzwAWq"
      },
      "id": "l4Ud1BHzwAWq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X_resampled)\n",
        "\n",
        "sample_size = min(3000, len(X_scaled))\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "rows = np.random.choice(len(X_scaled), sample_size, replace=False)\n",
        "\n",
        "X_sample = X_scaled[rows]\n",
        "y_sample = y_resampled.iloc[rows]"
      ],
      "metadata": {
        "id": "ZkbWkYyFwB3l"
      },
      "id": "ZkbWkYyFwB3l",
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "tsne = TSNE(\n",
        "    n_components=2,\n",
        "    perplexity=40,\n",
        "    max_iter=1000,\n",
        "    learning_rate='auto',\n",
        "    init='pca',\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "X_tsne = tsne.fit_transform(X_sample)"
      ],
      "metadata": {
        "id": "HsXanHRCwGVz"
      },
      "id": "HsXanHRCwGVz",
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tsne_df = pd.DataFrame({\n",
        "    \"TSNE1\": X_tsne[:, 0],\n",
        "    \"TSNE2\": X_tsne[:, 1],\n",
        "    \"attack_type\": y_sample.values\n",
        "})"
      ],
      "metadata": {
        "id": "sVPUWbLiwJGy"
      },
      "id": "sVPUWbLiwJGy",
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_attacks = tsne_df[\"attack_type\"].unique()\n",
        "\n",
        "color_names = [\n",
        "    \"blue\",\n",
        "    \"red\",\n",
        "    \"green\",\n",
        "    \"orange\",\n",
        "    \"purple\",\n",
        "    \"brown\",\n",
        "    \"pink\",\n",
        "    \"gray\"\n",
        "]\n",
        "\n",
        "color_dict = {\n",
        "    attack: color_names[i]\n",
        "    for i, attack in enumerate(unique_attacks)\n",
        "}\n",
        "\n",
        "print(\"Color Mapping:\")\n",
        "for attack, color in color_dict.items():\n",
        "    print(f\"{attack} → {color}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDUE8S7qwLHM",
        "outputId": "f268dc93-b55f-4c66-bc0a-1babd3dd2432"
      },
      "id": "JDUE8S7qwLHM",
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Color Mapping:\n",
            "R2L → blue\n",
            "Probe → red\n",
            "U2R → green\n",
            "normal → orange\n",
            "DoS → purple\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 7))\n",
        "\n",
        "for attack in unique_attacks:\n",
        "\n",
        "    # Filter for each attack type\n",
        "    subset = tsne_df[tsne_df[\"attack_type\"] == attack]\n",
        "\n",
        "    plt.scatter(\n",
        "        subset[\"TSNE1\"],\n",
        "        subset[\"TSNE2\"],\n",
        "        c=color_dict[attack],\n",
        "        label=attack,\n",
        "        alpha=0.6\n",
        "    )\n",
        "\n",
        "plt.xlabel(\"t-SNE Dimension 1\")\n",
        "plt.ylabel(\"t-SNE Dimension 2\")\n",
        "plt.title(\"t-SNE Visualization of Network Attack Types\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "VQCvKTqjwOdX",
        "outputId": "e1b11d09-5641-43b7-b2cf-eb07bcf42d77"
      },
      "id": "VQCvKTqjwOdX",
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import joblib\n",
        "import warnings\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.ensemble import IsolationForest, RandomForestClassifier\n",
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "from sklearn.svm import OneClassSVM\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, f1_score\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "plt.style.use(\"ggplot\")\n",
        "sns.set_palette(\"coolwarm\")"
      ],
      "metadata": {
        "id": "EoRJvrmUwRMo"
      },
      "id": "EoRJvrmUwRMo",
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "COL_NAMES = [\n",
        "    \"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\n",
        "    \"dst_bytes\",\"land\",\"wrong_fragment\",\"urgent\",\"hot\",\"num_failed_logins\",\n",
        "    \"logged_in\",\"num_compromised\",\"root_shell\",\"su_attempted\",\"num_root\",\n",
        "    \"num_file_creations\",\"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\n",
        "    \"is_host_login\",\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\",\n",
        "    \"srv_serror_rate\",\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\",\n",
        "    \"diff_srv_rate\",\"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\n",
        "    \"dst_host_same_srv_rate\",\"dst_host_diff_srv_rate\",\"dst_host_same_src_port_rate\",\n",
        "    \"dst_host_srv_diff_host_rate\",\"dst_host_serror_rate\",\"dst_host_srv_serror_rate\",\n",
        "    \"dst_host_rerror_rate\",\"dst_host_srv_rerror_rate\",\"label\",\"difficulty_level\"\n",
        "]\n",
        "\n",
        "df_raw = pd.read_csv(\"data/KDDTrain+.txt\", header=None, names=COL_NAMES)\n",
        "\n",
        "print(\"Dataset shape:\", df_raw.shape)\n",
        "df_raw.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "kyLoaMhgwSy_",
        "outputId": "46714e68-fe85-4754-8684-fb4811b9b4e1"
      },
      "id": "kyLoaMhgwSy_",
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset shape: (125973, 43)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
              "0         0           tcp  ftp_data   SF        491          0     0   \n",
              "1         0           udp     other   SF        146          0     0   \n",
              "2         0           tcp   private   S0          0          0     0   \n",
              "3         0           tcp      http   SF        232       8153     0   \n",
              "4         0           tcp      http   SF        199        420     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
              "0               0       0    0  ...                    0.17   \n",
              "1               0       0    0  ...                    0.00   \n",
              "2               0       0    0  ...                    0.10   \n",
              "3               0       0    0  ...                    1.00   \n",
              "4               0       0    0  ...                    1.00   \n",
              "\n",
              "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
              "0                    0.03                         0.17   \n",
              "1                    0.60                         0.88   \n",
              "2                    0.05                         0.00   \n",
              "3                    0.00                         0.03   \n",
              "4                    0.00                         0.00   \n",
              "\n",
              "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
              "0                         0.00                  0.00   \n",
              "1                         0.00                  0.00   \n",
              "2                         0.00                  1.00   \n",
              "3                         0.04                  0.03   \n",
              "4                         0.00                  0.00   \n",
              "\n",
              "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
              "0                      0.00                  0.05                      0.00   \n",
              "1                      0.00                  0.00                      0.00   \n",
              "2                      1.00                  0.00                      0.00   \n",
              "3                      0.01                  0.00                      0.01   \n",
              "4                      0.00                  0.00                      0.00   \n",
              "\n",
              "     label  difficulty_level  \n",
              "0   normal                20  \n",
              "1   normal                15  \n",
              "2  neptune                19  \n",
              "3   normal                21  \n",
              "4   normal                21  \n",
              "\n",
              "[5 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
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              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "      <th>label</th>\n",
              "      <th>difficulty_level</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>146</td>\n",
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              "      <td>0.60</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
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              "      <td>private</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "      <td>http</td>\n",
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              "      <td>232</td>\n",
              "      <td>8153</td>\n",
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              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8cf194d5-f619-4d88-a6a7-1f8b49a84cc3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8cf194d5-f619-4d88-a6a7-1f8b49a84cc3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8cf194d5-f619-4d88-a6a7-1f8b49a84cc3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_raw"
            }
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "attack_mapping = {\n",
        "    \"normal\": \"normal\",\n",
        "\n",
        "    # DoS\n",
        "    \"back\": \"DoS\", \"land\": \"DoS\", \"neptune\": \"DoS\", \"pod\": \"DoS\",\n",
        "    \"smurf\": \"DoS\", \"teardrop\": \"DoS\",\n",
        "\n",
        "    # Probe\n",
        "    \"satan\": \"Probe\", \"ipsweep\": \"Probe\", \"nmap\": \"Probe\", \"portsweep\": \"Probe\",\n",
        "\n",
        "    # R2L\n",
        "    \"guess_passwd\": \"R2L\", \"ftp_write\": \"R2L\", \"imap\": \"R2L\",\n",
        "    \"warezmaster\": \"R2L\", \"warezclient\": \"R2L\",\n",
        "\n",
        "    # U2R\n",
        "    \"buffer_overflow\": \"U2R\", \"loadmodule\": \"U2R\", \"perl\": \"U2R\", \"rootkit\": \"U2R\"\n",
        "}\n",
        "\n",
        "df_raw[\"attack_class\"] = df_raw[\"label\"].map(lambda x: attack_mapping.get(x, \"other\"))\n",
        "\n",
        "print(df_raw[\"attack_class\"].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VG45JrrwUu1",
        "outputId": "87ce6a98-d61d-47d2-90c9-549845262650"
      },
      "id": "6VG45JrrwUu1",
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "attack_class\n",
            "normal    67343\n",
            "DoS       45927\n",
            "Probe     11656\n",
            "R2L         982\n",
            "U2R          52\n",
            "other        13\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "y_master = le.fit_transform(df_raw[\"attack_class\"])\n",
        "\n",
        "print(\"Encoded classes:\", list(le.classes_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUs5eyaAwWqg",
        "outputId": "6585961b-ba4a-472f-c15c-db6ffd995835"
      },
      "id": "UUs5eyaAwWqg",
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoded classes: ['DoS', 'Probe', 'R2L', 'U2R', 'normal', 'other']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_raw = df_raw.drop(columns=[\"label\", \"attack_class\", \"difficulty_level\"], errors=\"ignore\")\n",
        "\n",
        "print(\"Raw feature shape:\", X_raw.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ojHostrawYKa",
        "outputId": "014c583f-6605-4c04-f8ee-ed5b9d9bcb85"
      },
      "id": "ojHostrawYKa",
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw feature shape: (125973, 41)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_cols = X_raw.select_dtypes(include=[\"int64\", \"float64\"]).columns\n",
        "X_numeric = X_raw[numeric_cols]\n",
        "\n",
        "print(\"Number of numeric columns:\", len(numeric_cols))\n",
        "print(\"Numeric data shape:\", X_numeric.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Lj06E1gwZg4",
        "outputId": "1db39eee-7fc2-4c93-f90d-b77381121627"
      },
      "id": "6Lj06E1gwZg4",
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of numeric columns: 38\n",
            "Numeric data shape: (125973, 38)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "preprocessing_pipeline = Pipeline(steps=[\n",
        "    (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
        "    (\"scaler\", StandardScaler()),\n",
        "    (\"pca\", PCA(n_components=20))\n",
        "])\n",
        "\n",
        "print(\"Preprocessing pipeline created successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-b6RSb9wbHL",
        "outputId": "f2784adf-8d28-4813-85b4-adf54139fc10"
      },
      "id": "r-b6RSb9wbHL",
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preprocessing pipeline created successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fit and transform\n",
        "X_processed = preprocessing_pipeline.fit_transform(X_numeric)\n",
        "\n",
        "print(\"Before preprocessing shape :\", X_numeric.shape)\n",
        "print(\"After preprocessing shape  :\", X_processed.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IcOa_DJ-wcp1",
        "outputId": "cf0bdda1-2ee2-42e5-fea3-ddbcb520a93d"
      },
      "id": "IcOa_DJ-wcp1",
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before preprocessing shape : (125973, 38)\n",
            "After preprocessing shape  : (125973, 20)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import joblib\n",
        "\n",
        "# Create the models directory if it doesn't exist\n",
        "models_dir = \"models\"\n",
        "if not os.path.exists(models_dir):\n",
        "    os.makedirs(models_dir)\n",
        "\n",
        "# Save the preprocessing pipeline (assuming 'preprocessing_pipeline' is defined globally)\n",
        "joblib.dump(preprocessing_pipeline, os.path.join(models_dir, \"preprocessing_engine.joblib\"))\n",
        "\n",
        "# Save the label encoder (assuming 'le' is defined globally)\n",
        "joblib.dump(le, os.path.join(models_dir, \"label_encoder.pkl\"))\n",
        "\n",
        "# Now attempt to load them (which should succeed after saving)\n",
        "prep_engine = joblib.load(os.path.join(models_dir, \"preprocessing_engine.joblib\"))\n",
        "label_encoder = joblib.load(os.path.join(models_dir, \"label_encoder.pkl\"))\n",
        "\n",
        "print(\"Prep engine loaded successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOblc6OywjEU",
        "outputId": "2efc9399-d835-4c9a-d042-9da8c6f78579"
      },
      "id": "QOblc6OywjEU",
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prep engine loaded successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: check which columns pipeline expects\n",
        "expected_cols = prep_engine.feature_names_in_\n",
        "\n",
        "# Step 2: select those columns from your encoded dataset\n",
        "X_numeric_again = X_resampled[expected_cols]\n",
        "\n",
        "# Step 3: apply preprocessing pipeline\n",
        "X_signal = prep_engine.transform(X_numeric_again)\n",
        "\n",
        "# Step 4: check shapes\n",
        "print(\"\\n--- Connection Architecture ---\")\n",
        "print(\"Pre-Selection Features :\", X_numeric_again.shape[1])\n",
        "print(\"Post-Selection Signal  :\", X_signal.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAEWA4X3wk6f",
        "outputId": "3dd2d0ba-6dc3-49cb-ced5-dc434f99a95e"
      },
      "id": "DAEWA4X3wk6f",
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Connection Architecture ---\n",
            "Pre-Selection Features : 38\n",
            "Post-Selection Signal  : 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "expected_cols = prep_engine.feature_names_in_"
      ],
      "metadata": {
        "id": "YbLmiCW50TB6"
      },
      "id": "YbLmiCW50TB6",
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "y_master = le.fit_transform(y_resampled)\n",
        "\n",
        "print(\"Encoded classes:\", list(le.classes_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXe21k3Y0VDP",
        "outputId": "d0cc8b61-a77e-4876-d949-10bdcc1538f8"
      },
      "id": "XXe21k3Y0VDP",
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoded classes: ['DoS', 'Probe', 'R2L', 'U2R', 'normal']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "\n",
        "print(\"\\n### ANALYZING NORMALCY BASELINES ###\")\n",
        "\n",
        "iso = IsolationForest(contamination=0.03, random_state=42)\n",
        "iso.fit(X_signal)\n",
        "\n",
        "iso_score = iso.decision_function(X_signal)\n",
        "iso_pred = iso.predict(X_signal)\n",
        "\n",
        "analysis_df = pd.DataFrame({\n",
        "    \"attack_class\": y_resampled.values,\n",
        "    \"iso_score\": iso_score,\n",
        "    \"iso_pred\": iso_pred\n",
        "})\n",
        "\n",
        "analysis_df[\"is_anomaly\"] = analysis_df[\"iso_pred\"].map({1: 0, -1: 1})\n",
        "\n",
        "print(\"Isolation Forest completed.\")\n",
        "print(analysis_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZp08KWF2dsQ",
        "outputId": "5dd58247-4bce-4889-ff3c-17295f30d251"
      },
      "id": "EZp08KWF2dsQ",
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "### ANALYZING NORMALCY BASELINES ###\n",
            "Isolation Forest completed.\n",
            "  attack_class  iso_score  iso_pred  is_anomaly\n",
            "0       normal   0.262531         1           0\n",
            "1       normal   0.140040         1           0\n",
            "2          DoS   0.267269         1           0\n",
            "3       normal   0.253784         1           0\n",
            "4       normal   0.263814         1           0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"len(y_resampled):\", len(y_resampled))\n",
        "print(\"len(iso_score):\", len(iso_score))\n",
        "print(\"len(iso_pred):\", len(iso_pred))\n",
        "print(\"len(X_signal):\", len(X_signal))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCSUp7PW20z1",
        "outputId": "65fa5567-147b-44a7-99ec-4a0c222bf9a1"
      },
      "id": "bCSUp7PW20z1",
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "len(y_resampled): 336715\n",
            "len(iso_score): 336715\n",
            "len(iso_pred): 336715\n",
            "len(X_signal): 336715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.violinplot(x=\"attack_class\", y=\"iso_score\", data=analysis_df, palette=\"magma\")\n",
        "plt.axhline(0, color=\"red\", linestyle=\"--\")\n",
        "plt.title(\"Isolation Scores by Attack Class\")\n",
        "plt.xlabel(\"Attack Class\")\n",
        "plt.ylabel(\"Isolation Score\")\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.countplot(x=\"attack_class\", hue=\"is_anomaly\", data=analysis_df)\n",
        "plt.title(\"Anomaly Labels vs Attack Categories\")\n",
        "plt.yscale(\"log\")\n",
        "plt.xlabel(\"Attack Class\")\n",
        "plt.ylabel(\"Count\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "bqi0NYZ523dW",
        "outputId": "c19e6f11-e5a9-45b0-e477-2f85874380cc"
      },
      "id": "bqi0NYZ523dW",
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X_signal,\n",
        "    y_resampled,\n",
        "    test_size=0.2,\n",
        "    random_state=42,\n",
        "    stratify=y_resampled\n",
        ")\n",
        "\n",
        "print(\"Training data shape:\", X_train.shape)\n",
        "print(\"Testing data shape:\", X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXzt54yC25LE",
        "outputId": "a24e951d-c41d-4941-f493-6f5027b42a78"
      },
      "id": "BXzt54yC25LE",
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (269372, 20)\n",
            "Testing data shape: (67343, 20)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(\n",
        "    n_estimators=100,\n",
        "    max_depth=10,\n",
        "    random_state=42,\n",
        "    n_jobs=-1,\n",
        "    class_weight=\"balanced\"\n",
        ")\n",
        "\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "print(\"Random Forest model trained successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjywZ7f_289J",
        "outputId": "456633f8-5884-4986-bafd-d559b4371add"
      },
      "id": "CjywZ7f_289J",
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest model trained successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\n",
        "    \"n_estimators\": [50, 100],\n",
        "    \"max_depth\": [10, 20],\n",
        "    \"min_samples_leaf\": [1, 2]\n",
        "}\n",
        "\n",
        "grid = GridSearchCV(\n",
        "    estimator=RandomForestClassifier(\n",
        "        random_state=42,\n",
        "        n_jobs=-1,\n",
        "        class_weight=\"balanced\"\n",
        "    ),\n",
        "    param_grid=param_grid,\n",
        "    cv=3,\n",
        "    scoring=\"f1_macro\",\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "grid.fit(X_train, y_train)\n",
        "\n",
        "best_model = grid.best_estimator_\n",
        "\n",
        "print(\"Best Parameters:\", grid.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_h57fYv92_F0",
        "outputId": "59c9629a-7c4d-491c-fbd9-8ab860f59965"
      },
      "id": "_h57fYv92_F0",
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
            "Best Parameters: {'max_depth': 20, 'min_samples_leaf': 1, 'n_estimators': 100}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = best_model.predict(X_test)\n",
        "\n",
        "print(\"Prediction completed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wtp85MGf3BEh",
        "outputId": "e4066b5f-9ff9-4ea3-a6a8-b9dbfd70719e"
      },
      "id": "Wtp85MGf3BEh",
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction completed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "print(\"Model Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Macro F1 Score:\", f1_score(y_test, y_pred, average=\"macro\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWCFydVs3EKE",
        "outputId": "eb424b01-a264-477b-df48-5737ffd9f11e"
      },
      "id": "XWCFydVs3EKE",
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Accuracy: 0.9942533002687733\n",
            "Macro F1 Score: 0.994247879772532\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
        "\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "JOwQlfbT3FuE",
        "outputId": "14af6f4d-ffed-4aac-9e83-cacda5aa199b"
      },
      "id": "JOwQlfbT3FuE",
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "report = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "audit_df = pd.DataFrame(report).transpose()\n",
        "\n",
        "print(audit_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrXApJds3HSW",
        "outputId": "49e9d326-06ea-4928-8c96-f8e71404e907"
      },
      "id": "JrXApJds3HSW",
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score       support\n",
            "DoS            0.994224  0.996807  0.995514  13469.000000\n",
            "Probe          0.994292  0.995842  0.995066  13468.000000\n",
            "R2L            0.996207  0.994580  0.995393  13469.000000\n",
            "U2R            0.994162  0.998886  0.996519  13469.000000\n",
            "normal         0.992371  0.985150  0.988747  13468.000000\n",
            "accuracy       0.994253  0.994253  0.994253      0.994253\n",
            "macro avg      0.994251  0.994253  0.994248  67343.000000\n",
            "weighted avg   0.994251  0.994253  0.994248  67343.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "audit_df.iloc[:-3][\"f1-score\"].plot(kind=\"barh\", figsize=(8,5))\n",
        "\n",
        "plt.title(\"Attack Detection Performance (F1 Score)\")\n",
        "plt.xlabel(\"F1 Score\")\n",
        "plt.ylabel(\"Attack Type\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "1druAFtM3Itd",
        "outputId": "1c5b7702-21d9-4a33-bc53-d1e3d1b1741c"
      },
      "id": "1druAFtM3Itd",
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}