{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "56Fpqc7pZVSe" }, "outputs": [], "source": [ "# --------------------------------------------\n", "# Phishing URL Detection with Machine Learning\n", "# --------------------------------------------\n", "# scikit-learn\n", "# Step 1: Import Libraries\n", "import pandas as pd\n", "import numpy as np\n", "import re\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", "from sklearn.preprocessing import StandardScaler\n", "import re\n", "from urllib.parse import urlparse\n", "\n", "# For handling imbalance\n", "from imblearn.over_sampling import SMOTE" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Vzzvmru6keGN", "outputId": "37a58e80-16fb-49a9-b925-1251a62d6db3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "👩‍💻 Loading dataset...\n", "📊 Dataset Shape: (549346, 2)\n", "📝 Available columns: ['URL', 'Label']\n", "\n", "🧹 Cleaning dataset...\n", "✅ Data cleaned! Remaining rows: 549346\n" ] } ], "source": [ "# Step 2: Load Dataset\n", "data = pd.read_csv(\"phishing_site_urls.csv\")\n", "\n", "print(\"👩‍💻 Loading dataset...\")\n", "print(\"📊 Dataset Shape:\", data.shape)\n", "print(\"📝 Available columns:\", data.columns.tolist())\n", "\n", "# Step 2a: Ensure correct column names\n", "possible_url_cols = ['url', 'URL', 'Url', 'Domain', 'Link']\n", "possible_label_cols = ['label', 'Label', 'target', 'Target', 'class']\n", "\n", "url_col = next((col for col in data.columns if col in possible_url_cols), None)\n", "label_col = next((col for col in data.columns if col in possible_label_cols), None)\n", "\n", "if url_col is None:\n", " raise ValueError(\"⚠️ No URL column found! Please check your dataset.\")\n", "if label_col is None:\n", " raise ValueError(\"⚠️ No label column found! Please check your dataset.\")\n", "\n", "# Rename for consistency\n", "data.rename(columns={url_col: 'url', label_col: 'label'}, inplace=True)\n", "\n", "# Step 2b: Clean missing values\n", "print(\"\\n🧹 Cleaning dataset...\")\n", "data.dropna(subset=['url', 'label'], inplace=True)\n", "\n", "# Step 2c: Convert labels to numeric if needed\n", "if data['label'].dtype == 'object':\n", " data['label'] = data['label'].map({\n", " 'phishing': 1, 'Phishing': 1, 'bad': 1, 'malicious': 1,\n", " 'legitimate': 0, 'Legitimate': 0, 'good': 0, 'safe': 0\n", " }).fillna(data['label'])\n", "\n", "data['label'] = pd.to_numeric(data['label'], errors='coerce')\n", "data.dropna(subset=['label'], inplace=True)\n", "data['label'] = data['label'].astype(int)\n", "\n", "print(\"✅ Data cleaned! Remaining rows:\", data.shape[0])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EXDYMMc_kswg", "outputId": "14dd38ee-c5f0-4e36-8fe8-d3d81eb39487" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "🔎 Extracting features...\n" ] } ], "source": [ "# Step 3: Feature Extraction\n", "print(\"\\n🔎 Extracting features...\")\n", "# Most popular words in phishing URLs\n", "def extract_features(url):\n", " features = {}\n", " \n", " try:\n", " parsed = urlparse(url)\n", " except:\n", " # If URL parsing fails, use original url\n", " parsed = None\n", " \n", " # Basic counts\n", " features['url_length'] = len(url)\n", " features['num_dots'] = url.count('.')\n", " features['num_hyphens'] = url.count('-')\n", " features['num_at'] = url.count('@')\n", " features['num_equal'] = url.count('=')\n", " features['num_slash'] = url.count('/')\n", " features['num_question'] = url.count('?')\n", " features['num_ampersand'] = url.count('&')\n", " features['num_percent'] = url.count('%')\n", " features['num_underscore'] = url.count('_')\n", " features['num_tilde'] = url.count('~')\n", " features['num_semicolon'] = url.count(';')\n", " \n", " # Check for www (case-insensitive, whole word)\n", " features['has_www'] = 1 if re.search(r'\\bwww\\b', url.lower()) else 0\n", " \n", " # Special characters count (excluding protocol and www prefix)\n", " url_without_protocol = re.sub(r'^https?://(www\\.)?', '', url, flags=re.IGNORECASE)\n", " features['num_special_chars'] = len(re.findall(r'[^a-zA-Z0-9]', url_without_protocol))\n", " \n", " # TLD detection (more accurate pattern)\n", " # Matches .com, .co.uk, .org, etc.\n", " tld_matches = re.findall(r'\\.[a-z]{2,}(?:\\.[a-z]{2,})?(?=[/?#]|$)', url.lower())\n", " features['num_tld'] = len(tld_matches)\n", " \n", " # Extract actual TLD (rightmost domain component)\n", " if parsed and parsed.netloc:\n", " domain_parts = parsed.netloc.lower().split('.')\n", " if len(domain_parts) >= 2:\n", " # Get TLD length (e.g., 'com' = 3, 'co.uk' = 5)\n", " features['tld_length'] = len(domain_parts[-1])\n", " # Check for suspicious/uncommon TLDs\n", " suspicious_tlds = ['.xyz', '.top', '.tk', '.ml', '.ga', '.cf', '.gq', '.pw', '.cc']\n", " features['has_suspicious_tld'] = 1 if any(tld in url.lower() for tld in suspicious_tlds) else 0\n", " else:\n", " features['tld_length'] = 0\n", " features['has_suspicious_tld'] = 0\n", " else:\n", " features['tld_length'] = 0\n", " features['has_suspicious_tld'] = 0\n", " \n", " # Subdomain counting (more accurate)\n", " if parsed and parsed.netloc:\n", " domain_parts = parsed.netloc.split('.')\n", " # Remove www from count if present\n", " if domain_parts and domain_parts[0].lower() == 'www':\n", " domain_parts = domain_parts[1:]\n", " # Subdomains = total parts - 2 (domain + tld) or - 3 for compound TLDs like .co.uk\n", " # Simplified: count dots in netloc minus 1 (for main domain.tld)\n", " features['num_subdomains'] = max(0, parsed.netloc.count('.') - 1)\n", " else:\n", " # Fallback to original logic\n", " features['num_subdomains'] = max(0, url.count('.') - 1)\n", " \n", " # ========== NEW: Main Domain Structure Analysis ==========\n", " if parsed and parsed.netloc:\n", " netloc = parsed.netloc.lower()\n", " # Remove port if present\n", " netloc_no_port = netloc.split(':')[0]\n", " domain_parts = netloc_no_port.split('.')\n", " \n", " # Extract main domain (second-level domain)\n", " if len(domain_parts) >= 2:\n", " # Skip www if present\n", " if domain_parts[0] == 'www':\n", " main_domain = domain_parts[1] if len(domain_parts) > 2 else domain_parts[0]\n", " else:\n", " main_domain = domain_parts[-2] # e.g., 'google' from 'www.google.com'\n", " \n", " # Main domain features\n", " features['domain_length'] = len(main_domain)\n", " features['domain_has_digits'] = 1 if any(c.isdigit() for c in main_domain) else 0\n", " features['domain_digit_count'] = sum(c.isdigit() for c in main_domain)\n", " features['domain_has_hyphen'] = 1 if '-' in main_domain else 0\n", " features['domain_hyphen_count'] = main_domain.count('-')\n", " \n", " # Domain entropy (randomness measure)\n", " def calculate_entropy(text):\n", " from collections import Counter\n", " import math\n", " if not text:\n", " return 0\n", " counts = Counter(text)\n", " length = len(text)\n", " entropy = -sum((count/length) * math.log2(count/length) for count in counts.values())\n", " return entropy\n", " \n", " features['domain_entropy'] = calculate_entropy(main_domain)\n", " \n", " # Vowel/consonant ratio in domain (random domains often have unusual ratios)\n", " vowels = 'aeiou'\n", " vowel_count = sum(1 for c in main_domain if c in vowels)\n", " consonant_count = sum(1 for c in main_domain if c.isalpha() and c not in vowels)\n", " total_letters = vowel_count + consonant_count\n", " features['domain_vowel_ratio'] = vowel_count / total_letters if total_letters > 0 else 0\n", " \n", " # Check for repeated characters (e.g., 'gooogle', 'payppal')\n", " max_consecutive = max((len(list(group)) for char, group in __import__('itertools').groupby(main_domain)), default=0)\n", " features['domain_max_consecutive_chars'] = max_consecutive\n", " \n", " # Check if domain name mimics popular brands (typosquatting detection)\n", " popular_brands = ['google', 'facebook', 'paypal', 'amazon', 'microsoft', 'apple', \n", " 'netflix', 'instagram', 'twitter', 'linkedin', 'ebay', 'yahoo']\n", " # Calculate minimum edit distance to popular brands\n", " def levenshtein_distance(s1, s2):\n", " if len(s1) < len(s2):\n", " return levenshtein_distance(s2, s1)\n", " if len(s2) == 0:\n", " return len(s1)\n", " previous_row = range(len(s2) + 1)\n", " for i, c1 in enumerate(s1):\n", " current_row = [i + 1]\n", " for j, c2 in enumerate(s2):\n", " insertions = previous_row[j + 1] + 1\n", " deletions = current_row[j] + 1\n", " substitutions = previous_row[j] + (c1 != c2)\n", " current_row.append(min(insertions, deletions, substitutions))\n", " previous_row = current_row\n", " return previous_row[-1]\n", " \n", " min_brand_distance = min((levenshtein_distance(main_domain, brand) for brand in popular_brands), default=10)\n", " features['min_brand_edit_distance'] = min_brand_distance\n", " features['is_brand_typosquat'] = 1 if 0 < min_brand_distance <= 2 else 0\n", " \n", " else:\n", " features['domain_length'] = 0\n", " features['domain_has_digits'] = 0\n", " features['domain_digit_count'] = 0\n", " features['domain_has_hyphen'] = 0\n", " features['domain_hyphen_count'] = 0\n", " features['domain_entropy'] = 0\n", " features['domain_vowel_ratio'] = 0\n", " features['domain_max_consecutive_chars'] = 0\n", " features['min_brand_edit_distance'] = 10\n", " features['is_brand_typosquat'] = 0\n", " else:\n", " features['domain_length'] = 0\n", " features['domain_has_digits'] = 0\n", " features['domain_digit_count'] = 0\n", " features['domain_has_hyphen'] = 0\n", " features['domain_hyphen_count'] = 0\n", " features['domain_entropy'] = 0\n", " features['domain_vowel_ratio'] = 0\n", " features['domain_max_consecutive_chars'] = 0\n", " features['min_brand_edit_distance'] = 10\n", " features['is_brand_typosquat'] = 0\n", " \n", " # ========== END NEW FEATURES ==========\n", " \n", " # Character type counts\n", " features['num_numeric'] = len(re.findall(r'\\d', url))\n", " features['num_letters'] = len(re.findall(r'[a-zA-Z]', url))\n", " features['num_uppercase'] = len(re.findall(r'[A-Z]', url))\n", " features['num_lowercase'] = len(re.findall(r'[a-z]', url))\n", " \n", " # Ratios (avoid division by zero)\n", " url_len = features['url_length'] if features['url_length'] > 0 else 1\n", " features['digit_to_length_ratio'] = features['num_numeric'] / url_len\n", " features['letters_to_length_ratio'] = features['num_letters'] / url_len\n", " features['special_to_length_ratio'] = features['num_special_chars'] / url_len\n", " features['hyphens_to_length_ratio'] = features['num_hyphens'] / url_len\n", " features['dots_to_length_ratio'] = features['num_dots'] / url_len\n", " \n", " # Path and query length\n", " if parsed:\n", " features['path_length'] = len(parsed.path) if parsed.path else 0\n", " features['query_length'] = len(parsed.query) if parsed.query else 0\n", " features['hostname_length'] = len(parsed.netloc) if parsed.netloc else 0\n", " \n", " # NEW: Path depth (number of directories)\n", " features['path_depth'] = len([p for p in parsed.path.split('/') if p]) if parsed.path else 0\n", " \n", " # NEW: Number of parameters in query string\n", " features['num_query_params'] = len(parsed.query.split('&')) if parsed.query else 0\n", " \n", " # NEW: Fragment presence\n", " features['has_fragment'] = 1 if parsed.fragment else 0\n", " \n", " # NEW: Port presence and suspicious ports\n", " if parsed.port:\n", " features['has_custom_port'] = 1\n", " # Common suspicious ports for phishing\n", " features['has_suspicious_port'] = 1 if parsed.port in [8080, 8888, 3000, 4443, 5000] else 0\n", " else:\n", " features['has_custom_port'] = 0\n", " features['has_suspicious_port'] = 0\n", " else:\n", " features['path_length'] = 0\n", " features['query_length'] = 0\n", " features['hostname_length'] = 0\n", " features['path_depth'] = 0\n", " features['num_query_params'] = 0\n", " features['has_fragment'] = 0\n", " features['has_custom_port'] = 0\n", " features['has_suspicious_port'] = 0\n", " \n", " # Suspicious keywords (case-insensitive search)\n", " suspicious_keywords = [\n", " 'login', 'verify', 'update', 'bank', 'secure', 'account', \n", " 'free', 'bonus', 'click', 'offer', 'xml', 'php', 'exe', \n", " 'zip', 'wp', 'paypal', 'admin', 'amp', 'signin', 'password',\n", " 'confirm', 'suspended', 'billing', 'unlock', 'validate', 'authenticate'\n", " ]\n", " url_lower = url.lower()\n", " features['num_suspicious_words'] = sum(1 for kw in suspicious_keywords if kw in url_lower)\n", " features['has_suspicious_words'] = 1 if features['num_suspicious_words'] > 0 else 0\n", " \n", " # TLD checks (case-insensitive)\n", " popular_tlds = ['.com', '.org', '.net', '.edu', '.gov']\n", " features['has_popular_tld'] = 1 if any(tld in url_lower for tld in popular_tlds) else 0\n", " \n", " # Protocol checks\n", " features['has_https'] = 1 if url.lower().startswith('https://') else 0\n", " features['has_http'] = 1 if url.lower().startswith('http://') else 0\n", " \n", " # IP address in URL (potential phishing indicator)\n", " features['has_ip_address'] = 1 if re.search(r'(\\d{1,3}\\.){3}\\d{1,3}', url) else 0\n", " \n", " # Shortened URL services\n", " shorteners = ['bit.ly', 'tinyurl', 'goo.gl', 't.co', 'ow.ly', 'is.gd', 'buff.ly']\n", " features['is_shortened'] = 1 if any(short in url_lower for short in shorteners) else 0\n", " \n", " # NEW: Double slash in path (suspicious)\n", " features['has_double_slash'] = 1 if '//' in url.replace('://', '') else 0\n", " \n", " # NEW: Prefix/suffix separator (e.g., paypal-secure.com)\n", " if parsed and parsed.netloc:\n", " features['has_prefix_suffix'] = 1 if '-' in parsed.netloc else 0\n", " else:\n", " features['has_prefix_suffix'] = 0\n", " \n", " return features\n", "\n", "feature_list = [extract_features(u) for u in data['url']]\n", "features_df = pd.DataFrame(feature_list)\n", "features_df['label'] = data['label'].values" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_80803/687955899.py:6: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(x=word_freq.values, y=word_freq.index, palette='viridis')\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Most popular words in phishing URLs\n", "all_urls = ' '.join(data[data['label'] == 1]['url'].tolist()).lower()\n", "word_list = re.findall(r'\\b\\w+\\b', all_urls)\n", "word_freq = pd.Series(word_list).value_counts().head(10)\n", "plt.figure(figsize=(10,6))\n", "sns.barplot(x=word_freq.values, y=word_freq.index, palette='viridis')\n", "plt.title(\"Most Common Words in Phishing URLs\")\n", "plt.xlabel(\"Frequency\")\n", "plt.ylabel(\"Words\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "url_length", "rawType": "int64", "type": "integer" }, { "name": "num_dots", "rawType": "int64", "type": "integer" }, { "name": "num_hyphens", "rawType": "int64", "type": "integer" }, { "name": "num_at", "rawType": "int64", "type": "integer" }, { "name": "num_equal", "rawType": "int64", "type": "integer" }, { "name": "num_slash", "rawType": "int64", "type": "integer" }, { "name": "num_question", "rawType": "int64", "type": "integer" }, { "name": "num_ampersand", "rawType": "int64", "type": "integer" }, { "name": "num_percent", "rawType": "int64", "type": "integer" }, { "name": "num_underscore", "rawType": "int64", "type": "integer" }, { "name": "num_tilde", "rawType": "int64", "type": "integer" }, { "name": "num_semicolon", "rawType": "int64", "type": "integer" }, { "name": "has_www", "rawType": "int64", "type": "integer" }, { "name": "num_special_chars", "rawType": "int64", "type": "integer" }, { "name": "num_tld", "rawType": "int64", "type": "integer" }, { "name": "tld_length", "rawType": "int64", "type": "integer" }, { "name": "has_suspicious_tld", "rawType": "int64", "type": "integer" }, { "name": "num_subdomains", "rawType": "int64", "type": "integer" }, { "name": "domain_length", "rawType": "int64", "type": "integer" }, { "name": "domain_has_digits", "rawType": "int64", "type": "integer" }, { "name": "domain_digit_count", "rawType": "int64", "type": "integer" }, { "name": "domain_has_hyphen", "rawType": "int64", "type": "integer" }, { "name": "domain_hyphen_count", "rawType": "int64", "type": "integer" }, { "name": "domain_entropy", "rawType": "float64", "type": "float" }, { "name": "domain_vowel_ratio", "rawType": "float64", "type": "float" }, { "name": "domain_max_consecutive_chars", "rawType": "int64", "type": "integer" }, { "name": "min_brand_edit_distance", "rawType": "int64", "type": "integer" }, { "name": "is_brand_typosquat", "rawType": "int64", "type": "integer" }, { "name": "num_numeric", "rawType": "int64", "type": "integer" }, { "name": "num_letters", "rawType": "int64", "type": "integer" }, { "name": "num_uppercase", "rawType": "int64", "type": "integer" }, { "name": "num_lowercase", "rawType": "int64", "type": "integer" }, { "name": "digit_to_length_ratio", "rawType": "float64", "type": "float" }, { "name": "letters_to_length_ratio", "rawType": "float64", "type": "float" }, { "name": "special_to_length_ratio", "rawType": "float64", "type": "float" }, { "name": "hyphens_to_length_ratio", "rawType": "float64", "type": "float" }, { "name": "dots_to_length_ratio", "rawType": "float64", "type": "float" }, { "name": "path_length", "rawType": "int64", "type": "integer" }, { "name": "query_length", "rawType": "int64", "type": "integer" }, { "name": "hostname_length", "rawType": "int64", "type": "integer" }, { "name": "path_depth", "rawType": "int64", "type": "integer" }, { "name": "num_query_params", "rawType": "int64", "type": "integer" }, { "name": "has_fragment", "rawType": "int64", "type": "integer" }, { "name": "has_custom_port", "rawType": "int64", "type": "integer" }, { "name": "has_suspicious_port", "rawType": "int64", "type": "integer" }, { "name": "num_suspicious_words", "rawType": "int64", "type": "integer" }, { "name": "has_suspicious_words", "rawType": "int64", "type": "integer" }, { "name": "has_popular_tld", "rawType": "int64", "type": "integer" }, { "name": "has_https", "rawType": "int64", "type": "integer" }, { "name": "has_http", "rawType": "int64", "type": "integer" }, { "name": "has_ip_address", "rawType": "int64", "type": "integer" }, { "name": "is_shortened", "rawType": "int64", "type": "integer" }, { "name": "has_double_slash", "rawType": "int64", "type": "integer" }, { "name": "has_prefix_suffix", "rawType": "int64", "type": "integer" }, { "name": "label", "rawType": "int64", "type": "integer" } ], "ref": "1b5f2530-4ca6-48e3-9e8c-f7d8e9f78ed6", "rows": [ [ "0", "225", "6", "4", "0", "4", "10", "1", "3", "0", "4", "0", "0", "0", "32", "3", "0", "0", "5", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "58", "135", "2", "133", "0.2577777777777778", "0.6", "0.14222222222222222", "0.017777777777777778", "0.02666666666666667", "134", "90", "0", "9", "4", "0", "0", "0", "2", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "1", "81", "5", "2", "0", "2", "4", "0", "1", "0", "1", "0", "0", "1", "15", "3", "0", "0", "4", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "1", "65", "0", "65", "0.012345679012345678", "0.8024691358024691", "0.18518518518518517", "0.024691358024691357", "0.06172839506172839", "81", "0", "0", "5", "0", "0", "0", "0", "2", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "2", "177", "7", "1", "0", "0", "11", "0", "0", "0", "0", "0", "0", "0", "19", "1", "0", "0", "6", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "47", "111", "0", "111", "0.2655367231638418", "0.6271186440677966", "0.10734463276836158", "0.005649717514124294", "0.03954802259887006", "177", "0", "0", "11", "0", "0", "0", "0", "3", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "3", "60", "6", "0", "0", "0", "2", "0", "0", "0", "0", "0", "0", "1", "8", "3", "0", "0", "5", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "52", "0", "52", "0.0", "0.8666666666666667", "0.13333333333333333", "0.0", "0.1", "60", "0", "0", "3", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "4", "116", "1", "1", "0", "0", "10", "1", "0", "0", "0", "0", "0", "0", "13", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "21", "82", "12", "70", "0.1810344827586207", "0.7068965517241379", "0.11206896551724138", "0.008620689655172414", "0.008620689655172414", "79", "36", "0", "7", "1", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "1", "0", "1" ], [ "5", "36", "3", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "4", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "32", "0", "32", "0.0", "0.8888888888888888", "0.1111111111111111", "0.0", "0.08333333333333333", "36", "0", "0", "2", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "6", "61", "2", "3", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "7", "2", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "7", "47", "0", "47", "0.11475409836065574", "0.7704918032786885", "0.11475409836065574", "0.04918032786885246", "0.03278688524590164", "61", "0", "0", "3", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "7", "60", "5", "0", "0", "0", "6", "0", "0", "0", "0", "0", "0", "0", "11", "3", "0", "0", "4", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "4", "45", "0", "45", "0.06666666666666667", "0.75", "0.18333333333333332", "0.0", "0.08333333333333333", "60", "0", "0", "7", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "8", "19", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "2", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "4", "13", "0", "13", "0.21052631578947367", "0.6842105263157895", "0.10526315789473684", "0.0", "0.05263157894736842", "19", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "1" ], [ "9", "193", "4", "3", "0", "3", "10", "1", "1", "0", "3", "0", "1", "1", "26", "3", "0", "0", "3", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "35", "132", "0", "132", "0.18134715025906736", "0.6839378238341969", "0.13471502590673576", "0.015544041450777202", "0.02072538860103627", "100", "92", "0", "11", "2", "0", "0", "0", "4", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "10", "45", "3", "0", "0", "0", "3", "0", "0", "0", "0", "0", "0", "0", "6", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "39", "0", "39", "0.0", "0.8666666666666667", "0.13333333333333333", "0.0", "0.06666666666666667", "45", "0", "0", "4", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "11", "79", "2", "0", "0", "1", "2", "1", "0", "0", "0", "0", "0", "0", "6", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "7", "66", "12", "54", "0.08860759493670886", "0.8354430379746836", "0.0759493670886076", "0.0", "0.02531645569620253", "36", "42", "0", "3", "1", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "12", "63", "4", "1", "0", "0", "5", "1", "0", "0", "0", "0", "0", "1", "11", "2", "0", "0", "3", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "52", "2", "50", "0.0", "0.8253968253968254", "0.1746031746031746", "0.015873015873015872", "0.06349206349206349", "36", "26", "0", "3", "1", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "13", "59", "3", "0", "0", "0", "3", "0", "0", "0", "1", "0", "0", "1", "7", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "52", "0", "52", "0.0", "0.8813559322033898", "0.11864406779661017", "0.0", "0.05084745762711865", "59", "0", "0", "4", "0", "0", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "0", "1" ], [ "14", "64", "1", "1", "0", "0", "5", "0", "0", "0", "0", "0", "0", "0", "7", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "57", "0", "57", "0.0", "0.890625", "0.109375", "0.015625", "0.015625", "64", "0", "0", "5", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "15", "72", "3", "0", "0", "4", "2", "1", "3", "0", "1", "0", "3", "0", "17", "1", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "12", "43", "5", "38", "0.16666666666666666", "0.5972222222222222", "0.2361111111111111", "0.0", "0.041666666666666664", "24", "47", "0", "2", "4", "0", "0", "0", "2", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "16", "36", "3", "0", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "5", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "31", "0", "31", "0.0", "0.8611111111111112", "0.1388888888888889", "0.0", "0.08333333333333333", "36", "0", "0", "3", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "17", "53", "3", "0", "0", "0", "5", "0", "0", "0", "0", "0", "0", "0", "8", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "45", "0", "45", "0.0", "0.8490566037735849", "0.1509433962264151", "0.0", "0.05660377358490566", "53", "0", "0", "6", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "1", "0", "0", "1" ], [ "18", "37", "2", "0", "0", "0", "4", "0", "0", "0", "0", "0", "0", "1", "6", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "31", "0", "31", "0.0", "0.8378378378378378", "0.16216216216216217", "0.0", "0.05405405405405406", "37", "0", "0", "4", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "19", "63", "1", "1", "0", "2", "3", "0", "1", "0", "1", "0", "0", "0", "9", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "2", "52", "0", "52", "0.031746031746031744", "0.8253968253968254", "0.14285714285714285", "0.015873015873015872", "0.015873015873015872", "63", "0", "0", "3", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "20", "65", "5", "0", "0", "0", "6", "0", "0", "0", "0", "0", "0", "1", "11", "2", "0", "0", "4", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "4", "50", "0", "50", "0.06153846153846154", "0.7692307692307693", "0.16923076923076924", "0.0", "0.07692307692307693", "65", "0", "0", "6", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "21", "52", "3", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "5", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "47", "0", "47", "0.0", "0.9038461538461539", "0.09615384615384616", "0.019230769230769232", "0.057692307692307696", "52", "0", "0", "2", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "22", "59", "5", "0", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "7", "1", "0", "0", "4", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "4", "48", "0", "48", "0.06779661016949153", "0.8135593220338984", "0.11864406779661017", "0.0", "0.0847457627118644", "59", "0", "0", "2", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "23", "47", "2", "0", "0", "0", "4", "0", "0", "0", "0", "1", "0", "1", "7", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "40", "1", "39", "0.0", "0.851063829787234", "0.14893617021276595", "0.0", "0.0425531914893617", "47", "0", "0", "4", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "24", "49", "5", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "6", "1", "0", "0", "4", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "4", "39", "0", "39", "0.08163265306122448", "0.7959183673469388", "0.12244897959183673", "0.0", "0.10204081632653061", "49", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "25", "87", "2", "6", "0", "0", "7", "0", "0", "0", "0", "0", "0", "1", "15", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "2", "70", "0", "70", "0.022988505747126436", "0.8045977011494253", "0.1724137931034483", "0.06896551724137931", "0.022988505747126436", "87", "0", "0", "7", "0", "0", "0", "0", "2", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "26", "77", "2", "1", "0", "0", "7", "0", "0", "0", "0", "0", "0", "0", "10", "2", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "67", "0", "67", "0.0", "0.8701298701298701", "0.12987012987012986", "0.012987012987012988", "0.025974025974025976", "77", "0", "0", "8", "0", "0", "0", "0", "2", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "27", "12", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "2", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "2", "8", "1", "7", "0.16666666666666666", "0.6666666666666666", "0.16666666666666666", "0.0", "0.08333333333333333", "12", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "1" ], [ "28", "63", "5", "0", "0", "0", "6", "0", "0", "0", "0", "0", "0", "0", "11", "3", "0", "0", "4", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "4", "48", "0", "48", "0.06349206349206349", "0.7619047619047619", "0.1746031746031746", "0.0", "0.07936507936507936", "63", "0", "0", "7", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "29", "135", "11", "1", "0", "0", "3", "0", "0", "0", "0", "0", "0", "0", "15", "1", "0", "0", "10", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "27", "93", "0", "93", "0.2", "0.6888888888888889", "0.1111111111111111", "0.007407407407407408", "0.08148148148148149", "135", "0", "0", "3", "0", "0", "0", "0", "3", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "30", "29", "2", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "3", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "26", "0", "26", "0.0", "0.896551724137931", "0.10344827586206896", "0.0", "0.06896551724137931", "29", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "31", "188", "4", "3", "0", "3", "10", "1", "1", "0", "3", "0", "0", "1", "25", "3", "0", "0", "3", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "38", "125", "0", "125", "0.20212765957446807", "0.6648936170212766", "0.13297872340425532", "0.015957446808510637", "0.02127659574468085", "99", "88", "0", "11", "2", "0", "0", "0", "3", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "32", "95", "3", "6", "0", "0", "7", "0", "0", "0", "0", "0", "0", "1", "16", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "1", "78", "0", "78", "0.010526315789473684", "0.8210526315789474", "0.16842105263157894", "0.06315789473684211", "0.031578947368421054", "95", "0", "0", "8", "0", "0", "0", "0", "4", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "33", "58", "3", "0", "0", "0", "3", "0", "0", "0", "0", "0", "0", "0", "6", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "52", "0", "52", "0.0", "0.896551724137931", "0.10344827586206896", "0.0", "0.05172413793103448", "58", "0", "0", "4", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "34", "37", "2", "0", "0", "0", "3", "0", "0", "0", "0", "0", "0", "0", "5", "2", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "32", "0", "32", "0.0", "0.8648648648648649", "0.13513513513513514", "0.0", "0.05405405405405406", "37", "0", "0", "3", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "35", "66", "3", "0", "0", "0", "5", "0", "0", "0", "0", "0", "0", "0", "8", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "25", "33", "3", "30", "0.3787878787878788", "0.5", "0.12121212121212122", "0.0", "0.045454545454545456", "66", "0", "0", "5", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "1" ], [ "36", "94", "4", "0", "0", "1", "4", "1", "0", "0", "0", "0", "0", "0", "10", "2", "0", "0", "3", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "3", "81", "20", "61", "0.031914893617021274", "0.8617021276595744", "0.10638297872340426", "0.0", "0.0425531914893617", "51", "42", "0", "5", "1", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "37", "48", "4", "0", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "6", "2", "0", "0", "3", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "42", "0", "42", "0.0", "0.875", "0.125", "0.0", "0.08333333333333333", "48", "0", "0", "3", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "38", "27", "3", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1", "4", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "23", "0", "23", "0.0", "0.8518518518518519", "0.14814814814814814", "0.0", "0.1111111111111111", "27", "0", "0", "2", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "39", "47", "2", "1", "0", "0", "3", "0", "0", "0", "0", "0", "0", "0", "6", "2", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "1", "40", "0", "40", "0.02127659574468085", "0.851063829787234", "0.1276595744680851", "0.02127659574468085", "0.0425531914893617", "47", "0", "0", "4", "0", "0", "0", "0", "3", "1", "0", "0", "0", "0", "0", "0", "0", "1" ], [ "40", "33", "3", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "4", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "29", "0", "29", "0.0", "0.8787878787878788", "0.12121212121212122", "0.0", "0.09090909090909091", "33", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "1" ], [ "41", "90", "2", "0", "0", "1", "7", "0", "0", "0", "0", "0", "0", "1", "10", "1", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "4", "76", "0", "76", "0.044444444444444446", "0.8444444444444444", "0.1111111111111111", "0.0", "0.022222222222222223", "90", "0", "0", "7", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "42", "103", "10", "0", "0", "0", "3", "0", "0", "0", "0", "0", "0", "0", "13", "1", "0", "0", "9", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "17", "73", "1", "72", "0.1650485436893204", "0.7087378640776699", "0.1262135922330097", "0.0", "0.0970873786407767", "103", "0", "0", "3", "0", "0", "0", "0", "2", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "43", "19", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "2", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "17", "0", "17", "0.0", "0.8947368421052632", "0.10526315789473684", "0.0", "0.05263157894736842", "19", "0", "0", "2", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "1", "0", "0", "1" ], [ "44", "37", "3", "0", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "5", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "2", "30", "1", "29", "0.05405405405405406", "0.8108108108108109", "0.13513513513513514", "0.0", "0.08108108108108109", "37", "0", "0", "3", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "45", "13", "1", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", "2", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "11", "4", "7", "0.0", "0.8461538461538461", "0.15384615384615385", "0.0", "0.07692307692307693", "13", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "1" ], [ "46", "59", "2", "0", "0", "0", "4", "0", "0", "0", "0", "0", "0", "0", "6", "2", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "53", "1", "52", "0.0", "0.8983050847457628", "0.1016949152542373", "0.0", "0.03389830508474576", "59", "0", "0", "5", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "47", "42", "2", "0", "0", "0", "3", "0", "0", "0", "0", "0", "0", "0", "5", "2", "0", "0", "1", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "37", "0", "37", "0.0", "0.8809523809523809", "0.11904761904761904", "0.0", "0.047619047619047616", "42", "0", "0", "4", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "48", "78", "3", "0", "0", "0", "6", "0", "0", "0", "0", "0", "0", "0", "9", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "4", "65", "0", "65", "0.05128205128205128", "0.8333333333333334", "0.11538461538461539", "0.0", "0.038461538461538464", "78", "0", "0", "7", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ], [ "49", "50", "3", "0", "0", "0", "3", "0", "0", "0", "1", "0", "0", "0", "7", "2", "0", "0", "2", "0", "0", "0", "0", "0", "0.0", "0.0", "0", "10", "0", "0", "43", "0", "43", "0.0", "0.86", "0.14", "0.0", "0.06", "50", "0", "0", "4", "0", "0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "0", "0", "1" ] ], "shape": { "columns": 55, "rows": 549346 } }, "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
url_lengthnum_dotsnum_hyphensnum_atnum_equalnum_slashnum_questionnum_ampersandnum_percentnum_underscore...num_suspicious_wordshas_suspicious_wordshas_popular_tldhas_httpshas_httphas_ip_addressis_shortenedhas_double_slashhas_prefix_suffixlabel
02256404101304...2110000001
181520240101...2110000001
21777100110000...3110000001
360600020000...0010000001
41161100101000...1110000101
..................................................................
54934115300010000...0000010001
54934218110010000...0010000001
54934317110010000...0010000001
54934418110010000...0010000001
54934517110000000...0000000001
\n", "

549346 rows × 55 columns

\n", "
" ], "text/plain": [ " url_length num_dots num_hyphens num_at num_equal num_slash \\\n", "0 225 6 4 0 4 10 \n", "1 81 5 2 0 2 4 \n", "2 177 7 1 0 0 11 \n", "3 60 6 0 0 0 2 \n", "4 116 1 1 0 0 10 \n", "... ... ... ... ... ... ... \n", "549341 15 3 0 0 0 1 \n", "549342 18 1 1 0 0 1 \n", "549343 17 1 1 0 0 1 \n", "549344 18 1 1 0 0 1 \n", "549345 17 1 1 0 0 0 \n", "\n", " num_question num_ampersand num_percent num_underscore ... \\\n", "0 1 3 0 4 ... \n", "1 0 1 0 1 ... \n", "2 0 0 0 0 ... \n", "3 0 0 0 0 ... \n", "4 1 0 0 0 ... \n", "... ... ... ... ... ... \n", "549341 0 0 0 0 ... \n", "549342 0 0 0 0 ... \n", "549343 0 0 0 0 ... \n", "549344 0 0 0 0 ... \n", "549345 0 0 0 0 ... \n", "\n", " num_suspicious_words has_suspicious_words has_popular_tld \\\n", "0 2 1 1 \n", "1 2 1 1 \n", "2 3 1 1 \n", "3 0 0 1 \n", "4 1 1 1 \n", "... ... ... ... \n", "549341 0 0 0 \n", "549342 0 0 1 \n", "549343 0 0 1 \n", "549344 0 0 1 \n", "549345 0 0 0 \n", "\n", " has_https has_http has_ip_address is_shortened has_double_slash \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 1 \n", "... ... ... ... ... ... \n", "549341 0 0 1 0 0 \n", "549342 0 0 0 0 0 \n", "549343 0 0 0 0 0 \n", "549344 0 0 0 0 0 \n", "549345 0 0 0 0 0 \n", "\n", " has_prefix_suffix label \n", "0 0 1 \n", "1 0 1 \n", "2 0 1 \n", "3 0 1 \n", "4 0 1 \n", "... ... ... \n", "549341 0 1 \n", "549342 0 1 \n", "549343 0 1 \n", "549344 0 1 \n", "549345 0 1 \n", "\n", "[549346 rows x 55 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features_df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "numeric_cols = features_df.select_dtypes(include=[np.number]).columns\n", "correlation_values = features_df[numeric_cols].corr()['label'].drop(['label']).sort_values(ascending=False)\n", "correlation_values.plot(kind='barh', figsize=(10, 10))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Step 4: Feature Selection\n", "# Scaling features first\n", "# print(\"\\n⚙️ Scaling features...\")\n", "# scaler = StandardScaler()\n", "# features_scaled = scaler.fit_transform(features_df.drop('label', axis=1))\n", "# features_scaled_df = pd.DataFrame(features_scaled, columns=features_df.columns[:-1])\n", "\n", "# features_scaled_df" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Selecting features based on correlation\n", "# # No need to select features based on threshold since we are using all features as using all features has the best performance\n", "# selected_features = correlation_values[abs(correlation_values) > 0.1].index.tolist()\n", "# print(\"\\n🔧 Selected features based on correlation threshold:\", selected_features)\n", "# print(len(selected_features), \"features selected out of\", len(correlation_values))\n", "\n", "# features_scaled_df = features_scaled_df[selected_features]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cQSx2modk1q0", "outputId": "21efa0b0-a94c-4df2-8ae9-d58df68f2731" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "📐 Features Extracted: 54\n", "⚖️ Labels distribution before balancing:\n", " label\n", "0 392924\n", "1 156422\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# Step 4: Prepare Final Dataset\n", "X = features_df.drop('label', axis=1)\n", "y = data['label']\n", "\n", "print(\"📐 Features Extracted:\", X.shape[1])\n", "print(\"⚖️ Labels distribution before balancing:\\n\", y.value_counts())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TudR3kNSk9V4", "outputId": "027c7d25-7dca-42d7-ee73-f0461c69d315" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "⚖️ Labels distribution after balancing:\n", " label\n", "0 314339\n", "1 314339\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# Step 5: Train-Test Split\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=42, stratify=y\n", ")\n", "\n", "# Step 5b: Balance the training set using SMOTE\n", "sm = SMOTE(random_state=42)\n", "X_train, y_train = sm.fit_resample(X_train, y_train)\n", "\n", "print(\"⚖️ Labels distribution after balancing:\\n\", y_train.value_counts())\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "🤖 Training Random Forest model...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 1.2min\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Model training complete!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 2.4min finished\n" ] } ], "source": [ "print(\"\\n🤖 Training Random Forest model...\")\n", "rf_model = RandomForestClassifier(n_estimators=100, random_state=42, verbose=1)\n", "rf_model.fit(X_train, y_train)\n", "print(\"✅ Model training complete!\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "pNvP9eQAlN02" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 3.0s\n", "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 5.6s finished\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "📊 Model Evaluation\n", "🎯 Accuracy: 92.14 %\n", "\n", "📝 Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.94 0.94 78585\n", " 1 0.85 0.88 0.86 31285\n", "\n", " accuracy 0.92 109870\n", " macro avg 0.90 0.91 0.90 109870\n", "weighted avg 0.92 0.92 0.92 109870\n", "\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Step 7: Evaluation\n", "y_pred = rf_model.predict(X_test)\n", "accuracy = accuracy_score(y_test, y_pred)\n", "\n", "print(\"\\n📊 Model Evaluation\")\n", "print(\"🎯 Accuracy:\", round(accuracy * 100, 2), \"%\")\n", "print(\"\\n📝 Classification Report:\\n\", classification_report(y_test, y_pred))\n", "\n", "# Confusion Matrix\n", "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm, annot=True, fmt='d', cmap=\"Blues\")\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.title(\"Confusion Matrix\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ Saved tuned Random Forest to rf_tuned_model.joblib\n", "Loaded model type: \n", "Sample predictions: [0 1 1 0 0]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 0.0s finished\n" ] } ], "source": [ "# Step 6b: Save and load the tuned Random Forest model\n", "import joblib\n", "model_path = 'rf_tuned_model.joblib'\n", "joblib.dump(rf_model, model_path)\n", "print(f\"✅ Saved tuned Random Forest to {model_path}\")\n", "\n", "# Load back to verify\n", "loaded_rf = joblib.load(model_path)\n", "print(\"Loaded model type:\", type(loaded_rf))\n", "\n", "# Quick sanity check with a few test samples (if available)\n", "try:\n", " sample_input = X_test.iloc[:5]\n", "except Exception:\n", " sample_input = X_test[:5]\n", "\n", "if len(sample_input) > 0:\n", " sample_preds = loaded_rf.predict(sample_input)\n", " print(\"Sample predictions:\", sample_preds)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "hOt2i9A-lerL" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "👤 User: openai.com/verify\n", "🤖 Bot: ✅ Safe! This seems like a *Legitimate URL*. (Confidence: 51.0%)\n", "\n", "🔍 Debug Info:\n", " • URL Length: 17\n", " • Domain: 0 chars\n", " • Has HTTPS: 0\n", " • Suspicious Words: 1\n", " • Popular TLD: 1\n", " • Min Brand Distance: 10\n", " • Is Typosquat: 0\n", " • Subdomains: 0\n", "\n", " Probability [Legitimate, Phishing]: [0.51026536 0.48973464]\n", "\n", "👤 User: http://secure-login-bank.com/verify\n", "🤖 Bot: 🚨 Warning! This looks like a *Phishing URL*. (Confidence: 63.2%)\n", "\n", "🔍 Debug Info:\n", " • URL Length: 35\n", " • Domain: 17 chars\n", " • Has HTTPS: 0\n", " • Suspicious Words: 4\n", " • Popular TLD: 1\n", " • Min Brand Distance: 13\n", " • Is Typosquat: 0\n", " • Subdomains: 0\n", "\n", " Probability [Legitimate, Phishing]: [0.36786928 0.63213072]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 0.0s finished\n", "[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 0.0s finished\n", "[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 0.0s finished\n", "[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 0.0s\n", "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed: 0.0s finished\n" ] } ], "source": [ "# Step 8: Chat-Style URL Checker\n", "def check_url(url, debug=False):\n", " predict_features = extract_features(url)\n", " predict_features_df = pd.DataFrame([predict_features])\n", " \n", " # Transform with scaler (use transform, NOT fit_transform!)\n", " # predict_features_scaled = scaler.transform(predict_features_df)\n", " \n", " # Convert back to DataFrame with proper column names to avoid warning\n", " predict_features_scaled = pd.DataFrame(\n", " predict_features_df, \n", " columns=features_df.columns[:-1] # All columns except 'label'\n", " )\n", " \n", " # Make prediction with probability\n", " prediction = rf_model.predict(predict_features_scaled)[0]\n", " probabilities = rf_model.predict_proba(predict_features_scaled)[0]\n", " \n", " print(\"\\n👤 User:\", url)\n", " if prediction == 1:\n", " print(f\"🤖 Bot: 🚨 Warning! This looks like a *Phishing URL*. (Confidence: {probabilities[1]*100:.1f}%)\")\n", " else:\n", " print(f\"🤖 Bot: ✅ Safe! This seems like a *Legitimate URL*. (Confidence: {probabilities[0]*100:.1f}%)\")\n", " \n", " # Debug mode - show key features\n", " if debug:\n", " print(\"\\n🔍 Debug Info:\")\n", " print(f\" • URL Length: {predict_features['url_length']}\")\n", " print(f\" • Domain: {predict_features.get('domain_length', 'N/A')} chars\")\n", " print(f\" • Has HTTPS: {predict_features['has_https']}\")\n", " print(f\" • Suspicious Words: {predict_features['num_suspicious_words']}\")\n", " print(f\" • Popular TLD: {predict_features['has_popular_tld']}\")\n", " print(f\" • Min Brand Distance: {predict_features['min_brand_edit_distance']}\")\n", " print(f\" • Is Typosquat: {predict_features['is_brand_typosquat']}\")\n", " print(f\" • Subdomains: {predict_features['num_subdomains']}\")\n", " print(f\"\\n Probability [Legitimate, Phishing]: {probabilities}\")\n", "\n", "# Example chat tests\n", "check_url(\"openai.com/verify\", debug=True)\n", "check_url(\"http://secure-login-bank.com/verify\", debug=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🔍 Dataset Analysis:\n", "\n", "Total samples: 549346\n", "Legitimate: 392924 (71.5%)\n", "Phishing: 156422 (28.5%)\n", "\n", "HTTPS in legitimate: 0\n", "HTTPS in phishing: 7\n", "\n", "Average URL length (legitimate): 45.8\n", "Average URL length (phishing): 63.2\n", "\n", "📋 Sample legitimate URLs from dataset:\n", "[\"esxcc.com/js/index.htm?us.battle.net/noghn/en/?ref5M+9d'bbws&b`|tNEfnud&d\\xad\\rÅj\\x8fYÖJ\\x15p\\x94\\x92lR+CmP \\x0c!\\x10¡6\\x96Ô~PÖtÓ\\x9e¶P8~·Äëi\\x0f\\x12q°ôß|±½$~.ë½\\x1fü·ÝjQ³dì÷VTø\\x04\\x06srDiG[\\x80v\\x15' éò\\x12å¯ê\\x02YÃ\\x8eò\\x84\\x7f«Ù¾FkþàâFV#àÐØ\\x94}Ú\\x0b\\x83¨\\x08%¡\\x9a\\x90x(My¼RçÁs\\t²¹ù#Õ\\x15¦kI\\x11çzb\\x7fTUùØ9\\x88ÂEm© ÁqÚ'lMJ\\x92!c9o¼°iìTvµD\\x83Ã%&£.\\x90k\\x10&Ï\\x9dXO\\x1d\\x81O¾O©ªØ¤ÉÂ0¾cIJ¿\\rHi±æáÐ\\\\\\x1a/'\\x9b¡!¿F#\\t?ì.:\\x15â±¾«\\x10æD\\x85ô\\x841]͵¬6\\xa0\\x8bãê¤G;\\x81Ê\\x16ö2°ï¾øPü·\\x8b}9÷ð\\x99IP=\\x86â\\n\\x8eVz3¹ó¬éíA\\x08\\x8c vwK\\x8c}·Áªí_S¢D\\x07\\x837NØØòa0úqÞ\\x96ñ\\x0c\\x98f½tjÓµ»w2xhçt\\x9f\\\\I^u\\x96\\x80Æ\\x8a\\x9c!¯ì½ú\\xa0°S\\x01CYïõJï£l\\x1ad\\x19:6Õ±Ç4\\x04\\x08»ê\\x1bRùú\\x11#Òuü@^6\\x96amp;offerId=newmail-en-us-v2&siteState=ver:4\", \"www\\x0eeira¯&nvinip¿ncH¯wVö%ÆåyDaHðû/ÏyEùu\\x03Ë\\nÓ\\x176(rTÃ\\x06u\\x0f\\x8f\\x7fæ\\x82\\x0c\\x99=g\\x810¾\\x96÷mÖi\\x12Ó-;\\x9bXZ\\\\%êýü\\x05Éfn&\\x87\\\\°%7õÙ:¹u\\x96\\x0f\\x161ÌÑêFÄòW<\\x18\\x80$cï\\x86¦t[\\x910ò\\x9f>Þj\\x93®ÆeV2\\x92à\\x1bpù-íàÇ$E¤ZëȲú\\x16S̶\\tp\\x1fáSò°i°vþ[«³»]¹\\rjlÛW¿\\x95\\x9b\\x8a]ø¾µj¿;\\x08·ªo!\\x94ÒPì\\xa0·Ê\\x8bïH§#'\\x823\\x1bø@CÄR\\x02õ²çÇ\\x17\\x17Ý®\\x16ö\\x1d\\x7fQBÇÆg`Èå\\x85Zéê\\xa0D\\x1cîÂm\\x9e®ÎÝQó*x;9?\\x0fÁ\\x19\\x81Òâ\\x88\\x99bùt\\x1bÖ\\x07\\x96Ù®mÞ\\x80N\\x94\\x97P¯°^M\\x8eQ\\n(\\x1f-\\x04\\x06§;¬ÔAèUè\\x99é\\\\\\x10¨ø\\x95íÌ\\x88dB\\\\\\x01\\x8b\\x12[q½=ÿVuÃ\\x01»\\x07râæH\\x1bä\\x8dô/µ}\\x85»7!2=´ÂÏ4¿ª¡j91\\x04\\x86]\", \"'www.institutocgr.coo/web/media/syqvem/dk-\\x0fóij!\\x05R\\\\gr0âÕ5dfe<á0´¸KÕû¸\\x0b\\x92Ì\\x9cM\\x0e¹`Êoé\\x93DâÛ¼\\x1e,c\\x8d\\x83Ô\\x85¡æ>\\x981\\x08HÇÕ\\x1bdì³)e>eñâ\\x17û×\\x8eHËY¥U\\x91u´\\x187Ùm\\x12â?Já¥`Ó`\\x9d¤µ÷ïvºbsÔɦ§\\x7f+\\x97\\x14»ó¼:Uÿ\\x025kPÚG9\\x8càYàk\\x8c\\x17\\x9eÕ_DS\\x04g3`\\x08\\x8c\\x884\\x05ëeÏ\\x8e®\\x87ºx\\xadä{~£\\x84O\\x88nÁ\\x95ÓHµ\\x93ÔAi¡ý\\x98\\x9b\\x1c]\\x92û\\x83¦Pövr\\x17\\x1fÃ2\\x07¨ÛY\\x15Äðè7®/\", '¾5092', 'esxcc.com/js/index.htm?us.battle.net/login/en/?ref=ifezuzrus.battle.net/d3/en/in$ex&aoð;app\\x1dcïm-d3B\\x14\\x11 00-*\\x187-0\\r\\x10l1,\\x10\\x8c\\x11PhµW\\x11\\x0b\\x86\\x98;XyOy×vnÙ@áúß\\x12©¤4û\\x9a\\x083l|\\x9e\\x04Âú_Ù·X\\xa0kMôî}!ip\\x97«÷p7%\\x99Ø\\x9a\\x1drÀ\\x94ßW$\\x89õ·xIq\\x169E)\\xa0l¬0\\x99ZÝì\\x17\\x10\\x17Ãw!'Ôp\\x0e£ãW§&£Ñ\\x0bp&$ImÞôöÝYÖ\"]\n" ] } ], "source": [ "# Diagnostic: Check dataset for potential issues\n", "print(\"🔍 Dataset Analysis:\")\n", "print(f\"\\nTotal samples: {len(data)}\")\n", "print(f\"Legitimate: {(data['label']==0).sum()} ({(data['label']==0).sum()/len(data)*100:.1f}%)\")\n", "print(f\"Phishing: {(data['label']==1).sum()} ({(data['label']==1).sum()/len(data)*100:.1f}%)\")\n", "\n", "# Check if dataset has HTTPS URLs\n", "print(f\"\\nHTTPS in legitimate: {(features_df[features_df['label']==0]['has_https']).sum()}\")\n", "print(f\"HTTPS in phishing: {(features_df[features_df['label']==1]['has_https']).sum()}\")\n", "\n", "# Check URL lengths\n", "print(f\"\\nAverage URL length (legitimate): {features_df[features_df['label']==0]['url_length'].mean():.1f}\")\n", "print(f\"Average URL length (phishing): {features_df[features_df['label']==1]['url_length'].mean():.1f}\")\n", "\n", "# Sample some legitimate URLs from dataset\n", "print(\"\\n📋 Sample legitimate URLs from dataset:\")\n", "print(data[data['label']==0]['url'].head(10).tolist())" ] } ], "metadata": { "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Phishing URL", "language": "python", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 0 }