{ "cells": [ { "cell_type": "markdown", "id": "m0", "metadata": {}, "source": [ "# Final Project - Exploratory Data Analysis (EDA)\n", "\n", "## The data\n", "\n", "Two tables are read directly from the Hugging Face dataset repo\n", "[`Cyber-security-final-project/Generated_Injected_PDFs_HARMLESS`](https://huggingface.co/datasets/Cyber-security-final-project/Generated_Injected_PDFs_HARMLESS):\n", "\n", "* **`combined`** (`combined_features.csv`, 11,126 x 34) - the working table for this notebook. Each row\n", " is one PDF file, described by 32 structural features. It contains **two different populations**:\n", " * `source = \"Real\"` - 10,025 real PDFs from the CIC-Evasive-PDFMal2022 dataset.\n", " * `source = \"Synthetic\"` - 1,100 PDFs we generated ourselves by injecting harmless payloads.\n", "* **`manifest`** (`injection_manifest_combined.csv`, 1,100 x 11) - ground truth for the synthetic half:\n", " which payload was injected into which file. Loaded for reference; the analysis works on `combined`.\n", "\n", "**Target variable:** `Class` (Malicious / Benign). **Grouping variable:** `source` (Real / Synthetic).\n", "\n", "## How the project is organised\n", "\n", "**Part 1 - How similar is the synthetic malware to the real thing?** *(this notebook)*\n", "Data quality, cleaning, and a like-for-like comparison of the two corpora. It asks:\n", "\n", "1. How is the data split between the classes and between the two sources?\n", "2. Do the \"Real\" and \"Synthetic\" halves actually measure the same thing?\n", "3. Which features separate Malicious from Benign, and how strong is the separation?\n", "4. Do the features that work on real data also work on our synthetic data?\n", "\n", "**Part 2 - Evaluating AI models on the synthetic corpus** *(next stage)*\n", "Testing how well different models distinguish our injected PDFs from clean ones. Part 1 exists to\n", "establish how the results of Part 2 should be read - which is the subject of its conclusion.\n", "\n", "Each graph is followed by a **Findings** box.\n", "\n", "> **Note:** this notebook is read-only. It loads the two published tables, cleans a working copy\n", "> **in memory**, and writes nothing to disk. No new dataset is produced here." ] }, { "cell_type": "markdown", "id": "m1", "metadata": {}, "source": [ "---\n", "# Part 1 - How similar is the synthetic malware to the real thing?\n", "\n", "Before any model is evaluated, one question has to be settled: **is our synthetic corpus a stand-in\n", "for real malware, or is it something else?** The whole of Part 1 is an answer to that question.\n", "\n", "It proceeds in four steps. **Step 1** repairs the data far enough to be trusted at all - the raw\n", "table turns out to contain disguised missing values, an extractor crash written in as if it were\n", "data, and two columns measured in different units in each half. **Step 2** compares the two corpora\n", "visually, six graphs, each drawn for both halves side by side. **Step 3** summarises what the\n", "comparison found, and **Step 4** draws the conclusion that decides how Part 2 must be run.\n", "\n", "The short version of the answer, established from several independent angles below: the two corpora\n", "do **not** share a data-generating process, and that turns out to be the reason the synthetic corpus\n", "is worth evaluating on rather than a reason to discard it." ] }, { "cell_type": "markdown", "id": "m2", "metadata": {}, "source": [ "## Step 0 - Setup and loading" ] }, { "cell_type": "code", "execution_count": 1, "id": "c3", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:00.938287Z", "iopub.status.busy": "2026-07-31T14:05:00.937679Z", "iopub.status.idle": "2026-07-31T14:05:03.652360Z", "shell.execute_reply": "2026-07-31T14:05:03.650718Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "combined : (11126, 34)\n", "manifest : (1100, 11)\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " Fine name pdfsize metadata size pages xref Length title characters isEncrypted embedded files images text \\\n", "0 aedaf3c5428a2e3ba600c44b96ad78dfdf8ed76e7df129... 8.0 180.0 1.0 11.0 0.0 0.0 0.0 0 No \n", "1 fe767fb2584a10c010626263ea950643ac25f6ca24628f... 15.0 224.0 0.0 20.0 7.0 0.0 0.0 0 No \n", "2 544c5223ee301affad514b6fa585b3191625aba0a7222b... 4.0 468.0 2.0 13.0 16.0 0.0 0.0 0 Yes \n", "3 669772e626deccb9cfb7eb6a61e13d248d0ea08f1abe15... 17.0 250.0 1.0 15.0 0.0 0.0 0.0 0 No \n", "4 e434c884f45a691b0bf33d765f61794007eb0b8bb9f590... 7.0 252.0 3.0 16.0 45.0 0.0 0.0 0 Yes \n", "\n", " header obj endobj stream endstream xref trailer startxref pageno encrypt ObjStm JS Javascript AA OpenAction Acroform JBIG2Decode RichMedia launch \\\n", "0 \\t%PDF-1.3 10 10 3.0 3 1 1.0 1 1 0.0 0.0 1 1 0 1 0 0 0 0 \n", "1 \\t%PDF-1.6 19 19 9.0 9 1 1.0 1 1 0.0 0.0 0 0 0 0 1 0 0 0 \n", "2 \\t%PDF-1.3 12 12 3.0 3 1 1.0 1 2 0.0 0.0 1 2 0 1 0 0 0 0 \n", "3 \\t%PDF-1.3 14 14 2.0 2 1 1.0 1 1 0.0 0.0 2 3 0 1 1 0 0 0 \n", "4 \\t%PDF-1.3 15 15 4.0 4 1 1.0 1 3 0.0 0.0 1 2 0 1 0 0 0 0 \n", "\n", " EmbeddedFile XFA Colors Class source \n", "0 0 0 0.0 Malicious Real \n", "1 8 1 0.0 Malicious Real \n", "2 0 0 0.0 Malicious Real \n", "3 0 0 0.0 Malicious Real \n", "4 0 0 0.0 Malicious Real " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Global seed, shared with the Part 2 notebook, so every stochastic step here\n", "# (UMAP layouts, any sampling) reproduces on a re-run.\n", "SEED = 42\n", "random.seed(SEED)\n", "np.random.seed(SEED)\n", "\n", "sns.set_theme(style=\"whitegrid\")\n", "plt.rcParams[\"figure.figsize\"] = (10, 5)\n", "plt.rcParams[\"axes.titlesize\"] = 13\n", "pd.set_option(\"display.max_columns\", 60)\n", "pd.set_option(\"display.width\", 160)\n", "\n", "HF_BASE = (\"https://huggingface.co/datasets/Cyber-security-final-project/\"\n", " \"Generated_Injected_PDFs_HARMLESS/resolve/main/Datasets/\")\n", "\n", "combined = pd.read_csv(HF_BASE + \"combined_features.csv\")\n", "manifest = pd.read_csv(HF_BASE + \"injection_manifest_combined.csv\")\n", "\n", "print(\"combined :\", combined.shape)\n", "print(\"manifest :\", manifest.shape)\n", "combined.head()" ] }, { "cell_type": "code", "execution_count": 2, "id": "c4", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:03.656421Z", "iopub.status.busy": "2026-07-31T14:05:03.655893Z", "iopub.status.idle": "2026-07-31T14:05:03.671745Z", "shell.execute_reply": "2026-07-31T14:05:03.670121Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Class Benign Malicious\n", "source \n", "Real 4468 5557\n", "Synthetic 200 900\n" ] } ], "source": [ "# The four groups we will compare throughout the notebook\n", "print(combined.groupby([\"source\", \"Class\"]).size().unstack(fill_value=0))" ] }, { "cell_type": "markdown", "id": "m5", "metadata": {}, "source": [ "## Step 1 - Data quality check\n", "\n", "### 1.1 - What are the columns, and what type did pandas give them?\n", "\n", "The very first thing to look at is the list of columns and the datatype pandas inferred for each one.\n", "The inferred type is a free diagnostic: a column that *should* be a number but arrived as text is\n", "telling us that something non-numeric is hiding inside it.\n", "\n", "Showing the *first* few values of each column would hide the problem - the first rows are ordinary\n", "numbers. So the profile below separates them: **`normal_values`** shows genuine numeric entries, and\n", "**`ODD_VALUES`** shows the non-numeric ones. The odd column is where the damage is visible." ] }, { "cell_type": "code", "execution_count": 3, "id": "c6", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:03.675071Z", "iopub.status.busy": "2026-07-31T14:05:03.674842Z", "iopub.status.idle": "2026-07-31T14:05:03.873716Z", "shell.execute_reply": "2026-07-31T14:05:03.872512Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns: 34 | numeric: 12 | text: 22\n" ] }, { "data": { "text/html": [ "
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sourcestr202Real, Synthetic
\n", "
" ], "text/plain": [ " dtype n_unique n_missing n_non_numeric normal_values ODD_VALUES\n", "column \n", "Fine name str 11126 0 11126 aedaf3c5428a2e3ba600c44b96ad78dfdf8ed76e7df129...\n", "pdfsize float64 1598 1 0 8.0, 15.0, 4.0 \n", "metadata size float64 421 1 0 180.0, 224.0, 468.0 \n", "pages float64 123 1 0 1.0, 0.0, 2.0 \n", "xref Length float64 1180 1 0 11.0, 20.0, 13.0 \n", "title characters float64 192 1 0 0.0, 7.0, 16.0 \n", "isEncrypted float64 6 1 0 0.0, 1.0, -1.0 \n", "embedded files float64 9 1 0 0.0, 1.0, -1.0 \n", "images str 122 1 1 0, -1, 15 1(1)\n", "text str 5 1 3 -1, 0 No, Yes, unclear\n", "header str 51 1 47 1, 0, -1 \\t%PDF-1.3, \\t%PDF-1.6, \\t%PDF-1.5\n", "obj str 547 3 3 10, 19, 12 (most, _Pro_Rodeo_Pix_, _Pro_Rodeo_Pix_'\n", "endobj str 546 3 1 10, 19, 12 pdfid.py\n", "stream float64 284 3 0 3.0, 9.0, 2.0 \n", "endstream str 287 3 2 3, 9, 2 pdfHeader), 1(1)\n", "xref str 22 3 1 1, 3, 0 pdfid.py\n", "trailer float64 21 3 0 1.0, 3.0, 46.0 \n", "startxref str 21 3 1 1, 3, 0 bytes[endHeader]\n", "pageno str 131 3 6 1, 2, 3 1(1), 53(2), 2(2)\n", "encrypt float64 5 3 0 0.0, 1.0, -1.0 \n", "ObjStm float64 65 3 0 0.0, 1.0, 2.0 \n", "JS str 32 3 3 1, 0, 2 1(1), 29(2), 2(2)\n", "Javascript str 35 3 6 1, 0, 2 2(1), 3(1), 1(1)\n", "AA str 41 3 1 0, 23, 7 1(1)\n", "OpenAction str 8 3 2 1, 0, 2 1(1), 12(2)\n", "Acroform str 10 3 1 0, 1, 2 1(1)\n", "JBIG2Decode str 21 3 1 0, 1, -1 1(1)\n", "RichMedia str 7 3 2 0, 4, 1 2(2), 1(1)\n", "launch str 5 3 1 0, 1, -1 1(1)\n", "EmbeddedFile str 19 3 2 0, 8, 9 1(1), 12(2)\n", "XFA str 7 3 1 0, 1, 2 1(1)\n", "Colors float64 132 3 0 0.0, -1.0, 9.0 \n", "Class str 2 1 2 Malicious, Benign\n", "source str 2 0 2 Real, Synthetic" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def profile(col):\n", " values = pd.unique(combined[col].dropna().astype(str))\n", " is_num = pd.to_numeric(pd.Series(values), errors=\"coerce\").notna().to_numpy()\n", " normal = list(values[is_num])[:3] # ordinary numeric values\n", " odd = list(values[~is_num])[:3] # anything that is NOT a number\n", " return pd.Series({\n", " \"dtype\": str(combined[col].dtype),\n", " \"n_unique\": combined[col].nunique(),\n", " \"n_missing\": int(combined[col].isna().sum()),\n", " \"n_non_numeric\": int((~is_num).sum()),\n", " \"normal_values\": \", \".join(normal),\n", " \"ODD_VALUES\": \", \".join(odd), # <- the junk hiding in the column\n", " })\n", "\n", "inventory = pd.DataFrame({c: profile(c) for c in combined.columns}).T\n", "inventory.index.name = \"column\"\n", "print(\"Columns:\", combined.shape[1], \"| numeric:\", (inventory[\"dtype\"] != \"str\").sum(),\n", " \"| text:\", (inventory[\"dtype\"] == \"str\").sum())\n", "inventory" ] }, { "cell_type": "code", "execution_count": 4, "id": "c7", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:03.877468Z", "iopub.status.busy": "2026-07-31T14:05:03.877001Z", "iopub.status.idle": "2026-07-31T14:05:03.912839Z", "shell.execute_reply": "2026-07-31T14:05:03.908609Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "obj -> ['(most', '_Pro_Rodeo_Pix_', \"_Pro_Rodeo_Pix_'\"]\n", "endobj -> ['pdfid.py']\n", "endstream -> ['pdfHeader)', '1(1)']\n", "xref -> ['pdfid.py']\n", "startxref -> ['bytes[endHeader]']\n", "pageno -> ['1(1)', '53(2)', '2(2)', '2(1)', '5(1)']\n", "JS -> ['1(1)', '29(2)', '2(2)']\n", "Javascript -> ['2(1)', '3(1)', '1(1)', '34(2)', '>']\n", "OpenAction -> ['1(1)', '12(2)']\n", "text -> ['No', 'Yes', 'unclear']\n" ] } ], "source": [ "# Zoom in on the strangest values in the table: the non-numeric entries of the count columns.\n", "suspect = [\"obj\", \"endobj\", \"stream\", \"endstream\", \"xref\", \"startxref\", \"pageno\",\n", " \"JS\", \"Javascript\", \"OpenAction\", \"text\"]\n", "for col in suspect:\n", " values = pd.unique(combined[col].dropna().astype(str))\n", " is_num = pd.to_numeric(pd.Series(values), errors=\"coerce\").notna().to_numpy()\n", " odd = list(values[~is_num])\n", " if odd:\n", " print(f\"{col:12s} -> {odd[:5]}\")" ] }, { "cell_type": "markdown", "id": "m8", "metadata": {}, "source": [ "**What the `ODD_VALUES` column reveals.** Three distinct kinds of non-number are sitting in columns\n", "that should contain nothing but counts:\n", "\n", "* `unclear`, `Yes`, `No` in `text` - a categorical column masquerading as a measurement.\n", "* `1(1)`, `29(2)`, `53(2)` in `JS`, `Javascript`, `OpenAction`, `pageno` - a second measurement\n", " packed into the same cell.\n", "* **`(most`, `pdfid.py`, `pdfHeader)`, `bytes[endHeader]`, `list` in `obj`, `endobj`, `endstream`,\n", " `xref`, `startxref`, `pageno`** - read those in order and the source is unmistakable: they are\n", " fragments of a Python traceback, *\"…(most recent call last)… pdfid.py … pdfHeader) …\n", " bytes[endHeader] … list\"*. The extraction script crashed on these files and its **error message was\n", " written into the CSV as if it were data**.\n", "\n", "The third case is the one that would have been easiest to miss and hardest to explain later: those\n", "cells are not measurements at all. Each of the three is repaired in 1.4." ] }, { "cell_type": "markdown", "id": "m9", "metadata": {}, "source": [ "### 1.2 - Fixing the column names\n", "\n", "Before documenting the columns we have to fix their **names**. The list printed above has four\n", "distinct problems, and they are worth taking seriously: every one of them is an invitation to a silent\n", "bug later on.\n", "\n", "**Problem 1 - an outright typo.** The identifier column is called **`Fine name`**. There is no such\n", "thing as a \"fine name\" - it is a mistyped `File name`. Left alone, it stays in every table and chart\n", "title for the rest of the project.\n", "\n", "**Problem 2 - spaces inside names.** `metadata size`, `xref Length`, `title characters` and\n", "`embedded files` contain spaces, so they can only ever be accessed as `df[\"xref Length\"]`, never as\n", "`df.xref_length`, and they break most model-export and formula interfaces.\n", "\n", "**Problem 3 - three different naming conventions in one table.** The columns arrived from two\n", "different extraction tools and it shows:\n", "`pdfsize` and `pages` are lowercase, `isEncrypted` is camelCase, `OpenAction`, `EmbeddedFile` and\n", "`RichMedia` are PascalCase, `JS` and `XFA` and `AA` are acronyms, and `xref Length` is a mix.\n", "Remembering which convention applies to which column is a pure waste of effort and a common source\n", "of `KeyError`.\n", "\n", "**Problem 4 - a genuinely dangerous pair.** The table contains **both `embedded files` and\n", "`EmbeddedFile`**. They are two *different* columns - the first counts files embedded in the document,\n", "the second counts occurrences of the `/EmbeddedFile` keyword - but they are distinguished only by a\n", "space and a capital letter. Confusing the two would be very easy and completely silent. We rename\n", "them to `embedded_files_count` and `keyword_embedded_file` so that they can never be mixed up.\n", "\n", "**The fix.** We convert everything to `snake_case`, correct the typo, and give the two ambiguous\n", "columns explicit names. Two more renames are for clarity: `text` becomes `has_text` (it holds\n", "`Yes`/`No`, not text) and `Class` becomes `label` (`class` is a reserved word in Python, and `Class`\n", "invites `df.Class` which shadows it)." ] }, { "cell_type": "code", "execution_count": 5, "id": "c10", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:03.916287Z", "iopub.status.busy": "2026-07-31T14:05:03.916011Z", "iopub.status.idle": "2026-07-31T14:05:03.926187Z", "shell.execute_reply": "2026-07-31T14:05:03.924713Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns not covered by the map: none\n", "Map entries with no matching column: none\n", "Duplicate target names: none\n", "\n", "Corrected column names:\n", "['file_name', 'pdf_size', 'metadata_size', 'pages', 'xref_length', 'title_characters', 'is_encrypted', 'embedded_files_count', 'images', 'has_text', 'pdf_header', 'obj', 'endobj', 'stream', 'endstream', 'xref', 'trailer', 'startxref', 'pageno', 'encrypt', 'objstm', 'js', 'javascript', 'aa', 'open_action', 'acroform', 'jbig2decode', 'rich_media', 'launch', 'keyword_embedded_file', 'xfa', 'colors', 'label', 'source']\n" ] } ], "source": [ "RENAME_MAP = {\n", " # --- identifiers and labels ---\n", " \"Fine name\": \"file_name\", # typo in the source file: \"Fine\" -> \"File\"\n", " \"Class\": \"label\", # 'class' is a Python keyword\n", " \"source\": \"source\",\n", " # --- file-level properties ---\n", " \"pdfsize\": \"pdf_size\",\n", " \"metadata size\": \"metadata_size\",\n", " \"pages\": \"pages\",\n", " \"xref Length\": \"xref_length\",\n", " \"title characters\": \"title_characters\",\n", " \"isEncrypted\": \"is_encrypted\",\n", " \"embedded files\": \"embedded_files_count\", # count of embedded files\n", " \"images\": \"images\",\n", " \"text\": \"has_text\", # holds Yes/No, not the text itself\n", " \"header\": \"pdf_header\",\n", " \"Colors\": \"colors\",\n", " # --- structural keyword counts ---\n", " \"obj\": \"obj\",\n", " \"endobj\": \"endobj\",\n", " \"stream\": \"stream\",\n", " \"endstream\": \"endstream\",\n", " \"xref\": \"xref\",\n", " \"trailer\": \"trailer\",\n", " \"startxref\": \"startxref\",\n", " \"pageno\": \"pageno\",\n", " \"encrypt\": \"encrypt\",\n", " \"ObjStm\": \"objstm\",\n", " # --- suspicious-behaviour flags ---\n", " \"JS\": \"js\",\n", " \"Javascript\": \"javascript\",\n", " \"AA\": \"aa\",\n", " \"OpenAction\": \"open_action\",\n", " \"Acroform\": \"acroform\",\n", " \"JBIG2Decode\": \"jbig2decode\",\n", " \"RichMedia\": \"rich_media\",\n", " \"launch\": \"launch\",\n", " \"EmbeddedFile\": \"keyword_embedded_file\", # count of the /EmbeddedFile keyword\n", " \"XFA\": \"xfa\",\n", "}\n", "\n", "# Safety check: the map must cover every column exactly once, with no duplicate targets.\n", "missing = set(combined.columns) - set(RENAME_MAP)\n", "extra = set(RENAME_MAP) - set(combined.columns)\n", "print(\"Columns not covered by the map:\", missing or \"none\")\n", "print(\"Map entries with no matching column:\", extra or \"none\")\n", "print(\"Duplicate target names:\",\n", " [n for n in RENAME_MAP.values() if list(RENAME_MAP.values()).count(n) > 1] or \"none\")\n", "\n", "combined = combined.rename(columns=RENAME_MAP)\n", "print(\"\\nCorrected column names:\")\n", "print(list(combined.columns))" ] }, { "cell_type": "markdown", "id": "m11", "metadata": {}, "source": [ "### 1.3 - Data dictionary\n", "\n", "Reading the inventory in 1.1: **22 of the 34 columns arrived as text**, including columns like `obj`,\n", "`js` and `open_action` that are obviously counts. That is the first warning sign. The table below\n", "documents every column under its **corrected name** - what it measures, in what unit, what the raw\n", "values actually look like, the datatype we got, and the datatype we want.\n", "\n", "*Group A - identifiers and labels (not features)*\n", "\n", "| # | Original name | Corrected name | What it measures | Unit | Data format in the file | Current dtype | Desired dtype |\n", "|---|---|---|---|---|---|---|---|\n", "| 1 | `Fine name` | `file_name` | File identifier (SHA-1 hash for real files, descriptive name for synthetic) | - | Hex string / `attack_source_NNNN.pdf` | `str` | `string` (ID, never a feature) |\n", "| 33 | `Class` | `label` | **Target variable** - is the file malicious? | - | `Malicious` / `Benign` | `str` | `category` |\n", "| 34 | `source` | `source` | Which corpus the row came from | - | `Real` / `Synthetic` | `str` | `category` |\n", "\n", "*Group B - file-level properties*\n", "\n", "| # | Original name | Corrected name | What it measures | Unit | Data format in the file | Current dtype | Desired dtype |\n", "|---|---|---|---|---|---|---|---|\n", "| 2 | `pdfsize` | `pdf_size` | Total size of the file | **KB for Real, bytes for Synthetic** | Number; `-1` = extraction failed | `float64` | `float64` (after rescaling to one unit) |\n", "| 3 | `metadata size` | `metadata_size` | Size of the document metadata block | bytes | Number; `-1` = failed; always ~1 for synthetic | `float64` | `Int64` |\n", "| 4 | `pages` | `pages` | Number of pages | count | Number; `-1` = failed | `float64` | `Int64` |\n", "| 5 | `xref Length` | `xref_length` | Number of entries in the cross-reference table | count | Number; `-1` = failed | `float64` | `Int64` |\n", "| 6 | `title characters` | `title_characters` | Length of the document title | characters | Number; `-1` = failed | `float64` | `Int64` |\n", "| 7 | `isEncrypted` | `is_encrypted` | Encryption markers found | count (0-4) | Number; `-1` = failed | `float64` | `Int64` |\n", "| 8 | `embedded files` | `embedded_files_count` | Files embedded inside the PDF | count | Number; `-1` = failed | `float64` | `Int64` |\n", "| 9 | `images` | `images` | Images in the document | count | Number; `-1` = failed; one stray `1(1)` | `str` | `Int64` |\n", "| 10 | `text` | `has_text` | Whether the document contains extractable text | - | `Yes` / `No` / **`unclear`** / `-1` / `0` | `str` | `category` (or boolean + missing) |\n", "| 11 | `header` | `pdf_header` | PDF version declared in the file header | - | `\\t%PDF-1.4`; `8` for all synthetic rows; junk like `\\ta` | `str` | `category` (extract version number) |\n", "| 32 | `Colors` | `colors` | Colour values referenced | count | Number; `-1` = failed | `float64` | `Int64` |\n", "\n", "*Group C - PDF structural keyword counts* (how many times each PDF syntax element appears)\n", "\n", "| # | Original name | Corrected name | What it measures | Unit | Data format in the file | Current dtype | Desired dtype |\n", "|---|---|---|---|---|---|---|---|\n", "| 12 | `obj` | `obj` | Object definitions opened | count | Number; `-1`; **crash text** in 24 rows (`(most`, `_Pro_Rodeo_Pix_`) | `str` | `Int64` |\n", "| 13 | `endobj` | `endobj` | Object definitions closed | count | Number; `-1`; **crash text** in 22 rows (`pdfid.py`) | `str` | `Int64` |\n", "| 14 | `stream` | `stream` | Data streams opened | count | Number; `-1` = failed | `float64` | `Int64` |\n", "| 15 | `endstream` | `endstream` | Data streams closed | count | Number; `-1`; one `1(1)`; crash text | `str` | `Int64` |\n", "| 16 | `xref` | `xref` | Cross-reference tables | count | Number; `-1`; **crash text** in 22 rows (`pdfid.py`) | `str` | `Int64` |\n", "| 17 | `trailer` | `trailer` | Trailer sections | count | Number; `-1` = failed | `float64` | `Int64` |\n", "| 18 | `startxref` | `startxref` | `startxref` pointers | count | Number; `-1`; **crash text** in 22 rows (`bytes[endHeader]`) | `str` | `Int64` |\n", "| 19 | `pageno` | `pageno` | Page-object references | count | Number; `-1`; `1(1)` notation (115 rows); crash text (22) | `str` | `Int64` |\n", "| 20 | `encrypt` | `encrypt` | `/Encrypt` dictionary references | count | Number; `-1` = failed | `float64` | `Int64` |\n", "| 21 | `ObjStm` | `objstm` | Object streams (can hide objects from scanners) | count | Number; `-1` = failed | `float64` | `Int64` |\n", "\n", "*Group D - suspicious-behaviour flags* (the security-relevant keywords)\n", "\n", "| # | Original name | Corrected name | What it measures | Unit | Data format in the file | Current dtype | Desired dtype |\n", "|---|---|---|---|---|---|---|---|\n", "| 22 | `JS` | `js` | `/JS` keyword - embedded JavaScript | count | Number; `-1`; `1(1)` notation (229 rows) | `str` | `Int64` |\n", "| 23 | `Javascript` | `javascript` | `/JavaScript` keyword | count | Number; `-1`; `1(1)` (117 rows) | `str` | `Int64` |\n", "| 24 | `AA` | `aa` | Additional-Actions - code on page events | count | Number; `-1`; `1(1)` | `str` | `Int64` |\n", "| 25 | `OpenAction` | `open_action` | Action executed automatically on open | count | Number; `-1`; `1(1)` (88 rows) | `str` | `Int64` |\n", "| 26 | `Acroform` | `acroform` | Interactive form present | count | Number; `-1`; `1(1)` | `str` | `Int64` |\n", "| 27 | `JBIG2Decode` | `jbig2decode` | JBIG2 filter (historically exploited) | count | Number; `-1`; `1(1)` | `str` | `Int64` |\n", "| 28 | `RichMedia` | `rich_media` | Embedded Flash / rich media | count | Number; `-1`; `1(1)` | `str` | `Int64` |\n", "| 29 | `launch` | `launch` | `/Launch` - runs an external program | count | Number; `-1`; `1(1)` | `str` | `Int64` |\n", "| 30 | `EmbeddedFile` | `keyword_embedded_file` | Occurrences of the `/EmbeddedFile` keyword | count | Number; `-1`; `1(1)` | `str` | `Int64` |\n", "| 31 | `XFA` | `xfa` | XML Forms Architecture | count | Number; `-1`; `1(1)` | `str` | `Int64` |\n", "\n", "**Three problems this table exposes, before a single graph is drawn:**\n", "\n", "1. **`-1` is a disguised missing value.** A count can never be negative, so every `-1` means\n", " \"the extraction tool failed here\", not \"zero\". Quantified in 1.5.\n", "2. **The `1(1)` notation.** The extractor (`pdfid`) writes `29(2)` to mean \"29 occurrences, 2 of them\n", " obfuscated\". It is a count *and* a second measurement crammed into one text cell - 638 cells over\n", " 339 rows, concentrated in `js` (229), `pageno` (115), `javascript` (104) and `open_action` (88).\n", "3. **Crash text from the tool itself** (`pdfid.py`, `bytes[endHeader]`, `(most`, `pdfHeader)`,\n", " `list`) fills 147 cells across 37 rows. These are fragments of a Python traceback - \"…(most recent\n", " call last)… pdfid.py…\" - written into the CSV instead of measurements.\n", "\n", "Section 1.4 repairs these one at a time." ] }, { "cell_type": "markdown", "id": "m12", "metadata": {}, "source": [ "### 1.4 - Repairing the columns\n", "\n", "The dictionary above identified specific, fixable defects. We now repair them in order, each in its\n", "own subsection, and each applied to the in-memory frame only." ] }, { "cell_type": "markdown", "id": "m13", "metadata": {}, "source": [ "#### 1.4.1 - Change 1: put file size in bytes\n", "\n", "The dictionary flagged that `pdf_size` is recorded in **kilobytes for the real files** and in\n", "**bytes for the synthetic files**. Two different units in one column is the kind of defect that\n", "never announces itself - every mean, median and correlation computed on the mixed column is\n", "meaningless, and a model would read the unit as if it were a feature.\n", "\n", "We standardise on **bytes**, because that is the unit that loses no precision: the real values are\n", "integers of kilobytes, so multiplying by 1,024 is exact, whereas dividing the synthetic byte counts\n", "by 1,024 would produce fractions.\n", "\n", "The `-1` sentinels must be left alone - multiplying a \"measurement failed\" marker by 1,024 would\n", "turn it into `-1024` and hide it from the sentinel check in 1.5." ] }, { "cell_type": "code", "execution_count": 6, "id": "c14", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:03.930671Z", "iopub.status.busy": "2026-07-31T14:05:03.930301Z", "iopub.status.idle": "2026-07-31T14:05:03.946410Z", "shell.execute_reply": "2026-07-31T14:05:03.945012Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BEFORE - median of pdf_size by source:\n", "source\n", "Real 36.0\n", "Synthetic 109148.0\n", "Name: pdf_size, dtype: float64\n", "\n", "Rows rescaled: 9716\n", "\n", "AFTER - median of pdf_size by source (bytes):\n", "source\n", "Real 36864.0\n", "Synthetic 109148.0\n", "Name: pdf_size, dtype: float64\n", "\n", "Sentinels preserved (count of -1): 302\n" ] } ], "source": [ "KB_TO_BYTES = 1024\n", "\n", "print(\"BEFORE - median of pdf_size by source:\")\n", "print(combined.groupby(\"source\")[\"pdf_size\"].median())\n", "\n", "# Scale ONLY the real rows, and ONLY genuine measurements (leave the -1 sentinels untouched).\n", "to_scale = (combined[\"source\"] == \"Real\") & (combined[\"pdf_size\"] > 0)\n", "combined.loc[to_scale, \"pdf_size\"] = combined.loc[to_scale, \"pdf_size\"] * KB_TO_BYTES\n", "\n", "print(\"\\nRows rescaled:\", int(to_scale.sum()))\n", "print(\"\\nAFTER - median of pdf_size by source (bytes):\")\n", "print(combined.groupby(\"source\")[\"pdf_size\"].median())\n", "print(\"\\nSentinels preserved (count of -1):\", int((combined[\"pdf_size\"] == -1).sum()))" ] }, { "cell_type": "markdown", "id": "m15", "metadata": {}, "source": [ "**Result.** 9,716 real rows were rescaled and all 302 sentinels survived untouched. The real median\n", "moves from **36 to 36,864 bytes** against **109,148 bytes** for the synthetic files. The absurd\n", "~3,000x gap collapses to about **3x**, which is a plausible, genuine difference: we injected payloads\n", "into full-length documents, while the CIC corpus contains many very small files.\n", "\n", "(These medians still include the `-1` sentinels, which drag the real figure down slightly. Once the\n", "sentinels are removed in 1.5 the real median is **38,912 bytes** - that is the number Graph 2 reports.)" ] }, { "cell_type": "markdown", "id": "m16", "metadata": {}, "source": [ "#### 1.4.2 - Change 2: mark the extractor's crash output with `-2`\n", "\n", "The dictionary found cells containing fragments of a Python traceback - `(most`, `pdfid.py`,\n", "`bytes[endHeader]`, `pdfHeader)`, `list`. These are not measurements at all: the extraction script hit\n", "an exception and its error message was written into the CSV.\n", "\n", "We give them their **own sentinel, `-2`**, so that the two kinds of failure stay distinguishable:\n", "\n", "* `-1` - the tool ran and reported that it could not measure this field.\n", "* `-2` - the tool **crashed**; whatever is in the cell is debris.\n", "\n", "Both are negative, so the single check in 1.5 still catches both at once, but keeping them apart means\n", "we can always ask how much of the missingness came from crashes rather than from ordinary failures.\n", "\n", "**Cells, not whole rows.** A crashed cell is detected as: not missing, not a number, and not the\n", "`n(m)` notation (which is a legitimate value handled in 1.4.4). Marking the *cell* rather than the\n", "whole row matters here - 22 rows carry the full six-field traceback and are already `-1` in every\n", "other column, so they end up fully marked either way; but 15 further rows have exactly **one** bad\n", "cell and are otherwise perfectly good records. Blanking those rows would throw away valid data to\n", "punish a single field." ] }, { "cell_type": "code", "execution_count": 7, "id": "c17", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:03.949956Z", "iopub.status.busy": "2026-07-31T14:05:03.949676Z", "iopub.status.idle": "2026-07-31T14:05:04.477690Z", "shell.execute_reply": "2026-07-31T14:05:04.476078Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Crash cells found: 147\n", "obj 24\n", "endobj 22\n", "endstream 22\n", "xref 22\n", "startxref 22\n", "pageno 22\n", "javascript 13\n", "\n", "Rows affected: 37\n", "Crash cells per affected row:\n", "1 15\n", "6 22\n", "\n", "Class of affected rows:\n", "label\n", "Malicious 35\n", "Benign 2\n", "\n", "Remaining non-numeric, non-'n(m)' cells: 0\n" ] } ], "source": [ "CRASH_SENTINEL = \"-2\"\n", "PAREN_PAT = r\"^\\s*\\d+\\(\\d+\\)\\s*$\" # the legitimate \"53(2)\" notation - NOT a crash\n", "\n", "feature_cols = [c for c in combined.columns\n", " if c not in [\"file_name\", \"label\", \"source\", \"pdf_header\", \"has_text\"]]\n", "\n", "def crashed(col):\n", " # A cell is crash debris if it is present, non-numeric, and not the n(m) notation.\n", " s = combined[col]\n", " numeric = pd.to_numeric(s, errors=\"coerce\").notna()\n", " paren = s.astype(\"string\").str.match(PAREN_PAT).fillna(False)\n", " return s.notna() & ~numeric & ~paren\n", "\n", "crash_mask = pd.concat({c: crashed(c) for c in feature_cols}, axis=1)\n", "\n", "print(\"Crash cells found:\", int(crash_mask.sum().sum()))\n", "print(crash_mask.sum()[lambda s: s > 0].sort_values(ascending=False).to_string())\n", "print(\"\\nRows affected:\", int(crash_mask.any(axis=1).sum()))\n", "print(\"Crash cells per affected row:\")\n", "print(crash_mask[crash_mask.any(axis=1)].sum(axis=1).value_counts().sort_index().to_string())\n", "print(\"\\nClass of affected rows:\")\n", "print(combined.loc[crash_mask.any(axis=1), \"label\"].value_counts(dropna=False).to_string())\n", "\n", "for col in feature_cols:\n", " if crash_mask[col].any():\n", " combined[col] = combined[col].astype(\"string\").mask(crash_mask[col], CRASH_SENTINEL)\n", "\n", "print(\"\\nRemaining non-numeric, non-'n(m)' cells:\",\n", " int(pd.concat({c: crashed(c) for c in feature_cols}, axis=1).sum().sum()))" ] }, { "cell_type": "markdown", "id": "m18", "metadata": {}, "source": [ "**Result.** **147 crash cells over 37 rows** are now `-2` instead of text. The split is exactly as\n", "described: 22 rows with six crash cells each (the full traceback across `obj`, `endobj`, `endstream`,\n", "`xref`, `startxref`, `pageno`) and 15 rows with a single bad cell. 35 of the 37 rows are Malicious -\n", "consistent with everything else we have seen, since malformed malicious PDFs are what break parsers.\n", "\n", "The crash text is now gone from the table, and no later section has to deal with it again." ] }, { "cell_type": "markdown", "id": "m19", "metadata": {}, "source": [ "#### 1.4.3 - Change 3: encode `has_text` as an integer\n", "\n", "`has_text` records whether the document contains extractable text, but it holds five different\n", "value types at once: `Yes`, `No`, `unclear`, `-1` and `0`. Because of the strings, pandas typed the\n", "whole column as text and it cannot be used in any calculation.\n", "\n", "The encoding we apply:\n", "\n", "| Raw value | Encoded as | Reasoning |\n", "|---|---|---|\n", "| `Yes` | `1` | text present |\n", "| `No` | `0` | no text |\n", "| `0` | `0` | already the numeric form of \"No\" (13 rows) |\n", "| `-1` | `-1` | extraction failed - kept as a sentinel so 1.5 catches it with all the others |\n", "| `unclear` | `` | the tool ran but could not decide. This is genuinely unknown, and unlike `-1` it is not a failure - so it becomes missing immediately |\n", "\n", "Note the target dtype: because the column must hold missing values, plain `int` is impossible in\n", "pandas (`int` has no NA). We use the **nullable `Int64`** type, which is an integer type that does\n", "allow ``." ] }, { "cell_type": "code", "execution_count": 8, "id": "c20", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:04.481636Z", "iopub.status.busy": "2026-07-31T14:05:04.481379Z", "iopub.status.idle": "2026-07-31T14:05:04.499502Z", "shell.execute_reply": "2026-07-31T14:05:04.497972Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BEFORE: str\n", "has_text\n", "No 5568\n", "Yes 4693\n", "unclear 549\n", "-1 302\n", "0 13\n", "NaN 1\n", "Name: count, dtype: int64\n", "\n", "AFTER: Int64\n", "has_text\n", "0 5581\n", "1 4693\n", " 550\n", "-1 302\n", "Name: count, dtype: Int64\n" ] } ], "source": [ "TEXT_MAP = {\"Yes\": 1, \"No\": 0, \"0\": 0, \"-1\": -1, \"unclear\": pd.NA}\n", "\n", "print(\"BEFORE:\", combined[\"has_text\"].dtype)\n", "print(combined[\"has_text\"].value_counts(dropna=False))\n", "\n", "combined[\"has_text\"] = (combined[\"has_text\"].astype(\"string\")\n", " .map(TEXT_MAP)\n", " .astype(\"Int64\"))\n", "\n", "print(\"\\nAFTER:\", combined[\"has_text\"].dtype)\n", "print(combined[\"has_text\"].value_counts(dropna=False))" ] }, { "cell_type": "markdown", "id": "m21", "metadata": {}, "source": [ "**Result.** The column is now `Int64` instead of text: **4,693 ones**, **5,581 zeros** (5,568 `No`\n", "plus the 13 numeric `0`s), **302** surviving `-1` sentinels, and **550** `` (the 549 `unclear`\n", "plus one blank row). `has_text` is now usable as a feature instead of being discarded as text - and\n", "in fact it enters the feature set in 1.5, where it turns out to be 7.6% missing once the sentinels\n", "are converted too." ] }, { "cell_type": "markdown", "id": "m22", "metadata": {}, "source": [ "#### 1.4.4 - Change 4: parse the `n(m)` notation out of **every** count column\n", "\n", "After 1.4.2 removed the crash debris, exactly one cause of text-typed columns remains: **the `n(m)`\n", "notation**. `pdfid` writes `53(2)` to mean \"53 occurrences, 2 of them obfuscated\" - two measurements\n", "crammed into one cell, which is enough to force the whole column to text.\n", "\n", "It appears in **638 cells spread over 13 columns**, not just the five structural ones:\n", "`js` (229), `pageno` (115), `javascript` (104), `open_action` (88), `keyword_embedded_file` (33),\n", "`xfa` (20), `aa` (14), `acroform` (13), `launch` (10), `jbig2decode` (6), `rich_media` (4),\n", "`endstream` (1) and `images` (1).\n", "\n", "We therefore apply the fix to **every feature column**, not a hand-picked list. Leaving any column\n", "out would mean silently dropping its `n(m)` rows to missing values later - and the worst-affected\n", "column, `js`, is one of the strongest predictors in the whole dataset. Treating some columns and not\n", "others would bias exactly the features we most want to measure.\n", "\n", "##### The fix: keep the count, discard the bracket\n", "\n", "We read `53(2)` as **53** and drop the `(2)`. The column then holds one clean quantity - the number\n", "of occurrences - and becomes `Int64` like every other count.\n", "\n", "**Why we discard the obfuscation number rather than keeping it as a feature.** It looks tempting:\n", "whether a keyword was deliberately obfuscated is exactly the kind of evasion signal a malware\n", "detector would want. But it is useless *for this project*, and the check below shows why:\n", "\n", "**every single `n(m)` cell in the table belongs to a Real file - all 638 of them, across 339 rows.\n", "The synthetic half contains none at all.**\n", "\n", "That is not a coincidence. Our generation script wrote its payloads in plain form and never applied\n", "the name-obfuscation tricks that `pdfid` counts, so there is nothing for the column to measure on\n", "that side of the data. An obfuscation feature would therefore be **structurally zero for the entire\n", "synthetic corpus** - and the central question of this notebook (Graph 5) is whether the real and\n", "synthetic halves behave alike. A feature that is present in one half and absent-by-construction in\n", "the other cannot answer that question; worse, it would separate the two halves perfectly and\n", "masquerade as signal. Keeping it would add a column that is guaranteed to mislead.\n", "\n", "If a later project works on the real corpus alone, parsing the bracket back out is a two-line change." ] }, { "cell_type": "code", "execution_count": 9, "id": "c23", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:04.502757Z", "iopub.status.busy": "2026-07-31T14:05:04.502452Z", "iopub.status.idle": "2026-07-31T14:05:05.171849Z", "shell.execute_reply": "2026-07-31T14:05:05.170448Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows containing 'n(m)' notation, by source:\n", "source\n", "Real 339\n", "\n", "'n(m)' counts recovered per column:\n", "js 229\n", "pageno 115\n", "javascript 104\n", "open_action 88\n", "keyword_embedded_file 33\n", "xfa 20\n", "aa 14\n", "acroform 13\n", "launch 10\n", "jbig2decode 6\n", "rich_media 4\n", "endstream 1\n", "images 1\n", "Total recovered: 638\n", "\n", "Dtypes after the fix:\n", "Int64 29\n", "\n", "Columns still typed as text: ['file_name', 'pdf_header', 'label', 'source']\n", "Table shape now: (11126, 34)\n" ] } ], "source": [ "PAREN = r\"^\\s*(\\d+)\\((\\d+)\\)\\s*$\" # matches the \"53(2)\" notation\n", "\n", "# Every column that is a measurement (i.e. everything except IDs, labels and the two\n", "# non-count columns). No hand-picked list: whatever carries the notation gets fixed.\n", "FIX_COLS = [c for c in combined.columns\n", " if c not in [\"file_name\", \"label\", \"source\", \"pdf_header\", \"has_text\"]]\n", "\n", "# First, the claim above: does any obfuscation notation appear in the synthetic half?\n", "paren_any = pd.concat({c: combined[c].astype(\"string\").str.match(PAREN).fillna(False)\n", " for c in FIX_COLS}, axis=1).any(axis=1)\n", "print(\"Rows containing 'n(m)' notation, by source:\")\n", "print(combined.loc[paren_any, \"source\"].value_counts().to_string())\n", "\n", "before = combined[FIX_COLS].dtypes.astype(str)\n", "recovered = {}\n", "\n", "for col in FIX_COLS:\n", " s = combined[col].astype(\"string\")\n", " count = s.str.extract(PAREN)[0] # the number BEFORE the bracket\n", " n_parsed = int(count.notna().sum())\n", "\n", " combined[col] = pd.to_numeric(s.where(count.isna(), count), errors=\"coerce\").astype(\"Int64\")\n", "\n", " if n_parsed:\n", " recovered[col] = n_parsed\n", "\n", "print(\"\\n'n(m)' counts recovered per column:\")\n", "print(pd.Series(recovered).sort_values(ascending=False).to_string())\n", "print(\"Total recovered:\", sum(recovered.values()))\n", "\n", "print(\"\\nDtypes after the fix:\")\n", "print(combined[FIX_COLS].dtypes.value_counts().to_string())\n", "print(\"\\nColumns still typed as text:\",\n", " [c for c in combined.columns if combined[c].dtype == \"str\"])\n", "print(\"Table shape now:\", combined.shape)" ] }, { "cell_type": "markdown", "id": "m24", "metadata": {}, "source": [ "**Result.** Every measurement column is now `Int64`. All **638** `n(m)` counts were recovered rather\n", "than lost - including the 229 in `js`, the single most affected column and one of the strongest\n", "predictors in the data. No rows were dropped and no columns were added.\n", "\n", "The only columns still typed as text are the ones that *should* be: `file_name`, `pdf_header`,\n", "`label` and `source`. Step 1 is finished - every feature column now holds nothing but integers and\n", "sentinels.\n", "\n", "The table below is the closing audit of Step 1: the datatype of every column before and after the\n", "repairs, next to the target set out in the data dictionary." ] }, { "cell_type": "code", "execution_count": 10, "id": "c25", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.175352Z", "iopub.status.busy": "2026-07-31T14:05:05.174827Z", "iopub.status.idle": "2026-07-31T14:05:05.194007Z", "shell.execute_reply": "2026-07-31T14:05:05.191318Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " dtype_before dtype_after changed\n", "column \n", "file_name str str \n", "pdf_size float64 Int64 yes\n", "metadata_size float64 Int64 yes\n", "pages float64 Int64 yes\n", "xref_length float64 Int64 yes\n", "title_characters float64 Int64 yes\n", "is_encrypted float64 Int64 yes\n", "embedded_files_count float64 Int64 yes\n", "images str Int64 yes\n", "has_text str Int64 yes\n", "pdf_header str str \n", "obj str Int64 yes\n", "endobj str Int64 yes\n", "stream float64 Int64 yes\n", "endstream str Int64 yes\n", "xref str Int64 yes\n", "trailer float64 Int64 yes\n", "startxref str Int64 yes\n", "pageno str Int64 yes\n", "encrypt float64 Int64 yes\n", "objstm float64 Int64 yes\n", "js str Int64 yes\n", "javascript str Int64 yes\n", "aa str Int64 yes\n", "open_action str Int64 yes\n", "acroform str Int64 yes\n", "jbig2decode str Int64 yes\n", "rich_media str Int64 yes\n", "launch str Int64 yes\n", "keyword_embedded_file str Int64 yes\n", "xfa str Int64 yes\n", "colors float64 Int64 yes\n", "label str str \n", "source str str \n", "\n", "Summary of final dtypes:\n", "Int64 30\n", "str 4\n", "\n", "Columns changed: 30 of 34\n" ] } ], "source": [ "# Datatype of every column: what we started with vs what we have now.\n", "before_dtypes = inventory[\"dtype\"].rename(index=RENAME_MAP) # original names -> corrected names\n", "\n", "dtypes_report = pd.DataFrame({\n", " \"dtype_before\": before_dtypes,\n", " \"dtype_after\": combined.dtypes.astype(str),\n", "})\n", "dtypes_report[\"changed\"] = np.where(\n", " dtypes_report[\"dtype_before\"] != dtypes_report[\"dtype_after\"], \"yes\", \"\")\n", "dtypes_report.index.name = \"column\"\n", "\n", "print(dtypes_report.to_string())\n", "print(\"\\nSummary of final dtypes:\")\n", "print(combined.dtypes.value_counts().to_string())\n", "print(\"\\nColumns changed:\", int((dtypes_report[\"changed\"] == \"yes\").sum()), \"of\", len(dtypes_report))" ] }, { "cell_type": "markdown", "id": "m26", "metadata": {}, "source": [ "#### 1.4.5 - Change 5: void `metadata_size` for the synthetic files\n", "\n", "The dictionary flagged that `metadata_size` looks wrong on the synthetic side. Comparing the two\n", "halves shows how wrong:\n", "\n", "| | Real | Synthetic |\n", "|---|---|---|\n", "| median | 265 | **1** |\n", "| 25th - 75th percentile | 180 - 319 | 0 - 2 |\n", "| maximum | 77,185 | **414** |\n", "| distinct values | 402 | 25 |\n", "\n", "Real files carry a metadata block of a few hundred bytes, which is what the column is supposed to\n", "measure. The synthetic column is not a smaller version of the same thing - it is **not a byte size at\n", "all**: 547 of the 1,100 files are `0`, another 358 are `2`, and the largest value in the entire\n", "corpus is 414. Our generation script wrote some small counter into this field instead of the\n", "metadata length.\n", "\n", "This one cannot be rescaled the way `pdf_size` was in 1.4.1, because there is no conversion between\n", "\"bytes of metadata\" and \"whatever this counter is\". The values are not merely inaccurate, they\n", "measure a different quantity - and a number that looks like a measurement but is not one is worse\n", "than no number at all: every median, correlation and box plot that touches it would be quietly wrong.\n", "\n", "**So we set `metadata_size` to `` for every synthetic row.** Missing is the honest encoding for\n", "\"we do not have this measurement\". The real half keeps its values and stays fully usable." ] }, { "cell_type": "code", "execution_count": 11, "id": "c27", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.197015Z", "iopub.status.busy": "2026-07-31T14:05:05.196614Z", "iopub.status.idle": "2026-07-31T14:05:05.222319Z", "shell.execute_reply": "2026-07-31T14:05:05.221372Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BEFORE - metadata_size by source:\n", " count 50% max\n", "source \n", "Real 10025.0 265.0 77185.0\n", "Synthetic 1100.0 1.0 414.0\n", "\n", "Rows voided: 1100\n", "\n", "AFTER - metadata_size by source:\n", " count 50% max\n", "source \n", "Real 10025.0 265.0 77185.0\n", "Synthetic 0.0 \n", "\n", "metadata_size missing overall: 9.9%\n" ] } ], "source": [ "print(\"BEFORE - metadata_size by source:\")\n", "print(combined.groupby(\"source\")[\"metadata_size\"].describe()[[\"count\", \"50%\", \"max\"]].round(1))\n", "\n", "is_synthetic = combined[\"source\"] == \"Synthetic\"\n", "combined.loc[is_synthetic, \"metadata_size\"] = pd.NA\n", "\n", "print(\"\\nRows voided:\", int(is_synthetic.sum()))\n", "print(\"\\nAFTER - metadata_size by source:\")\n", "print(combined.groupby(\"source\")[\"metadata_size\"].describe()[[\"count\", \"50%\", \"max\"]].round(1))\n", "print(\"\\nmetadata_size missing overall: \"\n", " f\"{combined['metadata_size'].isna().mean() * 100:.1f}%\")" ] }, { "cell_type": "markdown", "id": "m28", "metadata": {}, "source": [ "**Result.** All 1,100 synthetic values are now `` and the column is 9.9% missing overall. Nothing\n", "downstream will silently average a byte count together with a counter, and any comparison of\n", "`metadata_size` between the two sources is now impossible *by construction* rather than merely\n", "inadvisable - which is the point." ] }, { "cell_type": "markdown", "id": "m29", "metadata": {}, "source": [ "### 1.5 - Quantifying the disguised missing values\n", "\n", "Real-world security data is collected by an automatic extraction tool, and when that tool fails it\n", "writes placeholder values instead of leaving the cell empty. So `df.isna()` alone is not enough - we\n", "have to look for *disguised* missing values." ] }, { "cell_type": "code", "execution_count": 12, "id": "c30", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.227095Z", "iopub.status.busy": "2026-07-31T14:05:05.226591Z", "iopub.status.idle": "2026-07-31T14:05:05.236729Z", "shell.execute_reply": "2026-07-31T14:05:05.235598Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column dtypes after the 1.4 repairs:\n", "Int64 30\n", "str 4\n", "Name: count, dtype: int64\n", "\n", "Columns still typed as text:\n", "['file_name', 'pdf_header', 'label', 'source']\n", "\n", "Declared missing values (NaN/): 1723\n" ] } ], "source": [ "print(\"Column dtypes after the 1.4 repairs:\")\n", "print(combined.dtypes.value_counts())\n", "print()\n", "print(\"Columns still typed as text:\")\n", "print([c for c in combined.columns if combined[c].dtype == \"str\"])\n", "print()\n", "print(\"Declared missing values (NaN/):\", int(combined.isna().sum().sum()))" ] }, { "cell_type": "code", "execution_count": 13, "id": "c31", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.239465Z", "iopub.status.busy": "2026-07-31T14:05:05.238961Z", "iopub.status.idle": "2026-07-31T14:05:05.280396Z", "shell.execute_reply": "2026-07-31T14:05:05.279132Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns containing sentinels: 30 of 30\n", "Total sentinel cells: 15016\n", " of which -1 (measurement failed): 14869\n", " of which -2 (extractor crashed) : 147\n", "Distinct negative values found: [-2. -1.]\n" ] } ], "source": [ "# file_name is an ID, label/source are labels and pdf_header is a version string -\n", "# none of them are numeric features. has_text is now a proper Int64 column, so it stays.\n", "NON_FEATURES = [\"file_name\", \"label\", \"source\", \"pdf_header\"]\n", "\n", "df = combined.dropna(subset=[\"label\"]).copy() # drop the 1 broken row\n", "# float64 throughout: the nullable Int64 columns from 1.4 turn their into np.nan,\n", "# which keeps the comparisons below unambiguous.\n", "num = (df.drop(columns=NON_FEATURES)\n", " .apply(pd.to_numeric, errors=\"coerce\")\n", " .astype(\"float64\"))\n", "\n", "# Counts cannot be negative, so every negative value is a failure marker, not a measurement:\n", "# -1 = the tool reported it could not measure this field\n", "# -2 = the tool crashed (assigned in 1.4.2)\n", "neg_counts = (num < 0).sum()\n", "print(\"Columns containing sentinels:\", int((neg_counts > 0).sum()), \"of\", num.shape[1])\n", "print(\"Total sentinel cells:\", int((num < 0).sum().sum()))\n", "print(\" of which -1 (measurement failed):\", int((num == -1).sum().sum()))\n", "print(\" of which -2 (extractor crashed) :\", int((num == -2).sum().sum()))\n", "print(\"Distinct negative values found:\", np.unique(num.values[num.values < 0]))" ] }, { "cell_type": "code", "execution_count": 14, "id": "c32", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.282913Z", "iopub.status.busy": "2026-07-31T14:05:05.282676Z", "iopub.status.idle": "2026-07-31T14:05:05.302917Z", "shell.execute_reply": "2026-07-31T14:05:05.301512Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " clean row has >=1 sentinel % affected\n", "row_0 \n", "Real - Benign 4431 37 0.8\n", "Real - Malicious 4044 1513 27.2\n", "Synthetic - Benign 200 0 0.0\n", "Synthetic - Malicious 900 0 0.0\n" ] } ], "source": [ "# CRITICAL: is the extraction failure spread evenly, or concentrated in one group?\n", "group = df[\"source\"] + \" - \" + df[\"label\"]\n", "tab = pd.crosstab(group, (num < 0).any(axis=1))\n", "tab.columns = [\"clean row\", \"has >=1 sentinel\"]\n", "tab[\"% affected\"] = (tab[\"has >=1 sentinel\"] / tab.sum(axis=1) * 100).round(1)\n", "print(tab)" ] }, { "cell_type": "markdown", "id": "m33", "metadata": {}, "source": [ "#### Findings - data quality\n", "\n", "* The file *looks* almost perfect: it declared only 74 `NaN` out of ~378,000 cells. In reality\n", " **15,016 cells** are sentinels - **14,869** `-1` (measurement failed) and **147** `-2` (extractor\n", " crashed) - affecting **every one of the 30 feature columns**. A naive `df.isna().sum()` would have\n", " reported all of these as valid measurements, and every mean computed on them would be wrong.\n", "* **The failures are not random.** They hit **27.2% of Real-Malicious files** but only **0.8% of\n", " Real-Benign files**, and **0% of synthetic files**. The extraction tool breaks specifically on\n", " malformed malicious PDFs - and malicious PDFs are often *deliberately* malformed to defeat parsers.\n", "* This is a case of **informative missingness**: the fact that a value is missing is itself a strong\n", " clue about the class. It is tempting to exploit, but dangerous - a model that learns \"extraction\n", " failed, therefore malicious\" is learning about *our tool*, not about malware. We convert the\n", " sentinels to `NaN` and keep them out of the feature statistics." ] }, { "cell_type": "code", "execution_count": 15, "id": "c34", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.306429Z", "iopub.status.busy": "2026-07-31T14:05:05.305996Z", "iopub.status.idle": "2026-07-31T14:05:05.330916Z", "shell.execute_reply": "2026-07-31T14:05:05.329661Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing values after cleaning (top 8, %):\n", "metadata_size 12.60\n", "has_text 7.65\n", "images 7.65\n", "obj 7.16\n", "colors 5.09\n", "keyword_embedded_file 5.00\n", "javascript 5.00\n", "launch 5.00\n", "dtype: float64\n", "\n", "Working frame: (11125, 33)\n", "real: (10025, 33) | synthetic: (1100, 33)\n" ] } ], "source": [ "# Replace the sentinels with real missing values. This is our working frame - in memory only.\n", "clean = num.mask(num < 0)\n", "clean[\"label\"] = df[\"label\"].values\n", "clean[\"source\"] = df[\"source\"].values\n", "clean[\"is_malicious\"] = (clean[\"label\"] == \"Malicious\").astype(int)\n", "\n", "print(\"Missing values after cleaning (top 8, %):\")\n", "print((clean.drop(columns=[\"label\", \"source\"]).isna().mean() * 100).round(2)\n", " .sort_values(ascending=False).head(8))\n", "print()\n", "print(\"Working frame:\", clean.shape)\n", "\n", "# The two halves, used from here to the end of the notebook.\n", "real = clean[clean[\"source\"] == \"Real\"]\n", "synthetic = clean[clean[\"source\"] == \"Synthetic\"]\n", "print(\"real:\", real.shape, \"| synthetic:\", synthetic.shape)" ] }, { "cell_type": "markdown", "id": "m34a", "metadata": {}, "source": [ "### 1.6 - Duplicate rows\n", "\n", "Two things are commonly called \"a duplicate\", and in this table they mean very different things.\n", "An identifier appearing twice is a collection error - the same file counted twice, which inflates\n", "whichever group it lands in. Two *different* files producing an identical row of features is not an\n", "error at all: it means the CIC feature vocabulary cannot tell those two PDFs apart. The first must\n", "be removed, the second must be measured and understood, so we test for them separately." ] }, { "cell_type": "code", "execution_count": 16, "id": "c34b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Duplicated file_name values: 0\n", "\n", "Rows sharing their feature vector with >=1 other row: 543\n", "Redundant copies (rows a de-duplication would delete): 315\n", "\n", " unique shared % shared\n", "row_0 \n", "Real - Benign 4400 68 1.5\n", "Real - Malicious 5082 475 8.5\n", "Synthetic - Benign 200 0 0.0\n", "Synthetic - Malicious 900 0 0.0\n", "\n", "Distinct repeated fingerprints: 228\n", "Fingerprints spanning both labels : 0\n", "Fingerprints spanning both sources: 0\n", "\n", "Median pdf_size - shared rows: 10240.0 | unique rows: 47104.0\n" ] } ], "source": [ "# Duplicate check. Two different questions, asked separately:\n", "# 1. Is any PDF listed twice? -> duplicated file_name\n", "# 2. Do distinct PDFs share a feature row? -> duplicated feature vector\n", "# Run after 1.5 so the sentinels are already NA and cannot create false matches.\n", "FEATS = [c for c in clean.columns if c not in [\"label\", \"source\", \"is_malicious\"]]\n", "\n", "print(\"Duplicated file_name values:\", int(df[\"file_name\"].duplicated().sum()))\n", "print()\n", "\n", "dup_mask = clean[FEATS].duplicated(keep=False)\n", "print(\"Rows sharing their feature vector with >=1 other row:\", int(dup_mask.sum()))\n", "print(\"Redundant copies (rows a de-duplication would delete):\", int(clean[FEATS].duplicated().sum()))\n", "print()\n", "\n", "# Where do they sit? Same question we asked of the sentinels in 1.5.\n", "tab = pd.crosstab(clean[\"source\"] + \" - \" + clean[\"label\"], dup_mask)\n", "tab.columns = [\"unique\", \"shared\"]\n", "tab[\"% shared\"] = (tab[\"shared\"] / tab.sum(axis=1) * 100).round(1)\n", "print(tab)\n", "print()\n", "\n", "# The decisive test: does any repeated fingerprint carry two different labels\n", "# (a contradiction a model could never resolve) or straddle the two corpora?\n", "groups = clean[dup_mask].groupby(FEATS, dropna=False)\n", "print(\"Distinct repeated fingerprints:\", groups.ngroups)\n", "print(\"Fingerprints spanning both labels :\", int((groups[\"label\"].nunique() > 1).sum()))\n", "print(\"Fingerprints spanning both sources:\", int((groups[\"source\"].nunique() > 1).sum()))\n", "print()\n", "print(\"Median pdf_size - shared rows:\", clean.loc[dup_mask, \"pdf_size\"].median(),\n", " \"| unique rows:\", clean.loc[~dup_mask, \"pdf_size\"].median())" ] }, { "cell_type": "markdown", "id": "m34c", "metadata": {}, "source": [ "#### Findings - duplicates\n", "\n", "* **No file is counted twice.** All 11,125 `file_name` values are distinct SHA-256 hashes, so there\n", " is no collection error to repair and no row needs deleting on that ground.\n", "* **543 rows (4.9%) share their 30-feature vector with at least one other row**, collapsing into\n", " **228 distinct fingerprints**; de-duplicating would delete 315 rows. These are genuinely different\n", " PDFs - different hashes, different bytes - that the feature extractor describes identically.\n", "* **The collisions are concentrated exactly where the sentinels were.** They hit **8.5% of\n", " Real-Malicious** files against **1.5% of Real-Benign**, and **0% of both synthetic groups**. The\n", " shared rows are small files - median `pdf_size` **10,240 bytes** against **47,104** for the rest.\n", " A minimal one-page malicious PDF simply does not contain enough structure for 30 counters to\n", " distinguish it from the next one.\n", "* **No fingerprint carries two different labels, and none spans both corpora.** So the repetition\n", " introduces no label contradiction, and it cannot leak information between Real and Synthetic.\n", "\n", "**Decision: we keep every row.** Deleting them would discard 315 real malware samples on the basis\n", "of a limitation of the *measuring instrument* rather than of the data, and it would preferentially\n", "thin out the Real-Malicious group - the very group the sentinel analysis already showed to be the\n", "most fragile. But the number sets a ceiling worth remembering: on the Real half, roughly 5% of files\n", "are indistinguishable from another file using these features alone, so no classifier built on this\n", "vocabulary can be perfectly accurate no matter how good it is. That the synthetic half contains zero\n", "collisions is the first quantitative sign that our generated corpus is more internally varied, in\n", "feature terms, than the real one - Graph 7 returns to this." ] }, { "cell_type": "markdown", "id": "m34d", "metadata": {}, "source": [ "### 1.7 - Descriptive statistics\n", "\n", "With the columns repaired, the sentinels removed and the duplicates accounted for, this is the first\n", "point at which a summary table is meaningful. Computed before 1.4 it would have been arithmetic over\n", "kilobytes mixed with bytes and `-1` markers counted as measurements." ] }, { "cell_type": "code", "execution_count": 17, "id": "c34e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " count mean std min 25% 50% 75% max missing % skew\n", "pdf_size 10823.0 161788.26 1463064.94 0.0 11264.0 45056.0 88064.0 76885800.0 2.7 35.29\n", "metadata_size 9723.0 344.51 1588.86 4.0 180.0 268.0 325.0 77185.0 12.6 28.31\n", "pages 10823.0 4.57 21.83 0.0 1.0 1.0 3.0 1178.0 2.7 26.80\n", "xref_length 10823.0 2826.39 18804.30 0.0 13.0 27.0 172.5 589853.0 2.7 9.53\n", "title_characters 10823.0 51.20 1303.86 0.0 0.0 0.0 16.0 76993.0 2.7 39.58\n", "is_encrypted 10823.0 0.01 0.12 0.0 0.0 0.0 0.0 4.0 2.7 15.05\n", "embedded_files_count 10823.0 0.05 0.29 0.0 0.0 0.0 0.0 12.0 2.7 16.65\n", "images 10274.0 6.99 252.35 0.0 0.0 0.0 1.0 24204.0 7.6 88.34\n", "has_text 10274.0 0.46 0.50 0.0 0.0 0.0 1.0 1.0 7.6 0.17\n", "obj 10329.0 75.46 627.35 0.0 9.0 21.0 55.0 33217.0 7.2 41.75\n", "endobj 10587.0 75.43 648.26 0.0 9.0 20.0 54.0 33217.0 4.8 39.51\n", "stream 10582.0 27.32 363.82 0.0 2.0 6.0 21.0 32845.0 4.9 76.23\n", "endstream 10582.0 27.93 369.50 0.0 2.0 6.0 21.0 32845.0 4.9 73.31\n", "xref 10582.0 1.24 1.29 0.0 1.0 1.0 2.0 46.0 4.9 12.49\n", "trailer 10582.0 1.33 1.27 0.0 1.0 1.0 2.0 46.0 4.9 14.09\n", "startxref 10582.0 1.53 1.29 0.0 1.0 1.0 2.0 68.0 4.9 17.83\n", "pageno 10582.0 4.55 20.64 0.0 1.0 1.0 3.0 1178.0 4.9 28.02\n", "encrypt 10582.0 0.01 0.13 0.0 0.0 0.0 0.0 3.0 4.9 11.66\n", "objstm 10582.0 1.77 8.93 0.0 0.0 0.0 0.0 600.0 4.9 36.31\n", "js 10582.0 0.73 4.87 0.0 0.0 0.0 1.0 404.0 4.9 61.15\n", "javascript 10569.0 0.93 4.93 0.0 0.0 0.0 1.0 404.0 5.0 58.84\n", "aa 10569.0 0.31 5.44 0.0 0.0 0.0 0.0 213.0 5.0 29.89\n", "open_action 10569.0 0.32 0.50 0.0 0.0 0.0 1.0 12.0 5.0 2.22\n", "acroform 10569.0 0.38 0.66 0.0 0.0 0.0 1.0 8.0 5.0 1.98\n", "jbig2decode 10569.0 0.21 12.04 0.0 0.0 0.0 0.0 1178.0 5.0 91.26\n", "rich_media 10569.0 0.01 0.11 0.0 0.0 0.0 0.0 4.0 5.0 23.21\n", "launch 10569.0 0.02 0.14 0.0 0.0 0.0 0.0 2.0 5.0 7.19\n", "keyword_embedded_file 10569.0 0.55 2.02 0.0 0.0 0.0 0.0 17.0 5.0 3.78\n", "xfa 10569.0 0.06 0.25 0.0 0.0 0.0 0.0 5.0 5.0 4.95\n", "colors 10559.0 7.47 258.79 0.0 0.0 0.0 0.0 24345.0 5.1 82.32\n", "\n", "Features: 30\n", "Median of exactly 0: 18 features\n", "Skew > 10 : 23 features | most skewed: jbig2decode 91.3\n", "Constant features : none\n" ] } ], "source": [ "# One consolidated view of all 30 features after the 1.4 repairs and the 1.5 sentinel removal.\n", "# count/missing % come as a pair: a low count here means \"not measured\", not \"measured as zero\".\n", "summary = clean[FEATS].describe().T\n", "summary[\"missing %\"] = (clean[FEATS].isna().mean() * 100).round(1)\n", "summary[\"skew\"] = clean[FEATS].skew()\n", "\n", "print(summary.round(2).to_string())\n", "print()\n", "print(\"Features:\", len(FEATS))\n", "print(\"Median of exactly 0:\", int((summary[\"50%\"] == 0).sum()), \"features\")\n", "print(\"Skew > 10 :\", int((summary[\"skew\"] > 10).sum()), \"features\",\n", " \"| most skewed:\", summary[\"skew\"].idxmax(), round(summary[\"skew\"].max(), 1))\n", "print(\"Constant features :\", list(summary.index[summary[\"std\"] == 0]) or \"none\")" ] }, { "cell_type": "markdown", "id": "m34f", "metadata": {}, "source": [ "#### Findings - descriptive statistics\n", "\n", "* **Every feature is a heavily right-skewed count.** 23 of the 30 features have skew above 10, and\n", " `jbig2decode` reaches **91.3**. The mean is therefore useless as a typical value throughout this\n", " table: `images` averages 6.99 but its median is 0, because a single file carries 24,204 of them.\n", " Every comparison from here on uses medians, and Graph 2 plots `pdf_size` on a log axis for the\n", " same reason.\n", "* **18 of the 30 features have a median of exactly 0**, and many have a 75th percentile of 0 as well.\n", " These are the security flags - `js`, `launch`, `xfa`, `rich_media` - which are absent from most\n", " files by design. This is what makes the IQR rule collapse in 1.8: when Q1 and Q3 are both 0, the\n", " upper fence is 0 and every non-zero value is nominally an \"outlier\".\n", "* **No feature is constant**, so none can be dropped as uninformative before the analysis begins.\n", "* **`metadata_size` is the most-missing column at 12.6%**, which is the 1,100 synthetic rows we\n", " voided in 1.4.5 plus genuine extraction failures. The remaining columns run between 2.7% and 7.6%\n", " missing - all of it the sentinel damage quantified in 1.5, and all of it concentrated in the\n", " Real-Malicious group." ] }, { "cell_type": "markdown", "id": "m35", "metadata": {}, "source": [ "### 1.8 - Outlier treatment\n", "\n", "Every feature in this table is a count, and the maxima are extreme: a 23 MB PDF, a file with 263,987\n", "cross-reference entries, another with 76,993 title characters. The textbook reflex is to find outliers\n", "with the IQR rule and delete them. **We will not do that here**, and this subsection explains why -\n", "the decision is made on evidence, not on preference.\n", "\n", "The order of work matters: outliers can only be assessed *after* 1.5 converted the `-1`/`-2` sentinels\n", "to `NA`, otherwise every sentinel would register as an extreme low value." ] }, { "cell_type": "markdown", "id": "m36", "metadata": {}, "source": [ "#### 1.8.1 - Detect: how many outliers does the standard rule find?\n", "\n", "We use the conventional definition - a value above the **upper fence**, `Q3 + 1.5 x IQR`. Both halves\n", "are measured separately, because we already know they have different distributions." ] }, { "cell_type": "code", "execution_count": 18, "id": "c37", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.333896Z", "iopub.status.busy": "2026-07-31T14:05:05.333661Z", "iopub.status.idle": "2026-07-31T14:05:05.416758Z", "shell.execute_reply": "2026-07-31T14:05:05.415666Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "REAL half\n", " median upper_fence max n_outliers % of rows\n", "pdf_size 38912.0 192000.0 24387584.0 831.0 8.5\n", "pages 1.0 3.5 595.0 1812.0 18.6\n", "obj 19.0 116.5 7076.0 905.0 9.8\n", "stream 5.0 47.0 812.0 1017.0 10.7\n", "xref_length 22.0 187.0 263987.0 1699.0 17.5\n", "title_characters 0.0 35.0 76993.0 653.0 6.7\n", "images 0.0 2.5 898.0 1137.0 12.4\n", "colors 0.0 0.0 5682.0 815.0 8.6\n", "objstm 0.0 0.0 600.0 1799.0 19.0\n", "js 0.0 2.5 404.0 369.0 3.9\n", "\n", "SYNTHETIC half\n", " median upper_fence max n_outliers % of rows\n", "pdf_size 109148.0 713414.5 76885800.0 128.0 11.6\n", "pages 2.0 13.5 1178.0 139.0 12.6\n", "obj 34.0 174.5 33217.0 148.0 13.5\n", "stream 12.0 72.0 32845.0 124.0 11.3\n", "xref_length 469.0 1710.6 589853.0 154.0 14.0\n", "title_characters 11.0 115.0 1088.0 90.0 8.2\n", "images 1.0 7.5 24204.0 158.0 14.4\n", "colors 1.0 12.5 24345.0 182.0 16.5\n", "objstm 0.0 0.0 312.0 226.0 20.5\n", "js 0.0 0.0 15.0 183.0 16.6\n" ] } ], "source": [ "OUT_FEATURES = [\"pdf_size\", \"pages\", \"obj\", \"stream\", \"xref_length\",\n", " \"title_characters\", \"images\", \"colors\", \"objstm\", \"js\"]\n", "\n", "def outlier_table(frame):\n", " rows = {}\n", " for col in OUT_FEATURES:\n", " s = frame[col].dropna()\n", " q1, q3 = s.quantile([0.25, 0.75])\n", " fence = q3 + 1.5 * (q3 - q1)\n", " flagged = frame[frame[col] > fence]\n", " rows[col] = {\n", " \"median\": s.median(),\n", " \"upper_fence\": round(fence, 1),\n", " \"max\": s.max(),\n", " \"n_outliers\": len(flagged),\n", " \"% of rows\": round(100 * len(flagged) / len(s), 1),\n", " }\n", " return pd.DataFrame(rows).T\n", "\n", "print(\"REAL half\")\n", "print(outlier_table(real).to_string())\n", "print(\"\\nSYNTHETIC half\")\n", "print(outlier_table(synthetic).to_string())" ] }, { "cell_type": "markdown", "id": "m38", "metadata": {}, "source": [ "**Two things to notice before going further.**\n", "\n", "1. The rule flags a great deal of data - between 4% and 19% of rows depending on the feature. Removing\n", " the union of these would cost a large share of the dataset.\n", "2. For the **zero-inflated** columns the rule breaks down entirely. `colors` and `objstm` are `0` at\n", " the median *and* at the 75th percentile, so `Q3 + 1.5 x IQR` evaluates to **0** and the rule\n", " declares that *any non-zero value whatsoever* is an outlier. That is not a meaningful statement\n", " about the data - it is the IQR rule being applied to a distribution it was never designed for.\n", " Most of our features are counts that are mostly zero, so this caveat applies broadly." ] }, { "cell_type": "markdown", "id": "m39", "metadata": {}, "source": [ "#### 1.8.2 - Diagnose: are the outliers noise, or are they the signal?\n", "\n", "This is the question that decides the treatment. If outliers were measurement errors they would be\n", "scattered randomly across both classes. So for each feature we ask: **of the files flagged as\n", "outliers, what share is malicious?** - and compare that with the base rate of the corpus." ] }, { "cell_type": "code", "execution_count": 19, "id": "c40", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.419879Z", "iopub.status.busy": "2026-07-31T14:05:05.419654Z", "iopub.status.idle": "2026-07-31T14:05:05.488917Z", "shell.execute_reply": "2026-07-31T14:05:05.487488Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "REAL half - base rate = 55.4% malicious\n", " % malicious among outliers vs base rate\n", "js 91.3 35.9\n", "pdf_size 41.6 -13.8\n", "title_characters 30.5 -25.0\n", "colors 27.7 -27.7\n", "pages 23.0 -32.5\n", "xref_length 22.2 -33.2\n", "images 14.1 -41.4\n", "objstm 12.8 -42.6\n", "stream 8.1 -47.4\n", "obj 5.1 -50.3\n", "\n", "SYNTHETIC half - base rate = 81.8% malicious\n", " % malicious among outliers vs base rate\n", "js 97.3 15.4\n", "images 82.9 1.1\n", "pdf_size 82.8 1.0\n", "colors 82.4 0.6\n", "objstm 81.9 0.0\n", "stream 81.5 -0.4\n", "xref_length 81.2 -0.6\n", "obj 80.4 -1.4\n", "pages 79.1 -2.7\n", "title_characters 78.9 -2.9\n" ] } ], "source": [ "def enrichment(frame, name):\n", " base = 100 * (frame[\"label\"] == \"Malicious\").mean()\n", " rows = {}\n", " for col in OUT_FEATURES:\n", " s = frame[col].dropna()\n", " q1, q3 = s.quantile([0.25, 0.75])\n", " flagged = frame[frame[col] > q3 + 1.5 * (q3 - q1)]\n", " if len(flagged):\n", " pct = 100 * (flagged[\"label\"] == \"Malicious\").mean()\n", " rows[col] = {\"% malicious among outliers\": round(pct, 1),\n", " \"vs base rate\": round(pct - base, 1)}\n", " out = pd.DataFrame(rows).T.sort_values(\"% malicious among outliers\", ascending=False)\n", " print(f\"{name} - base rate = {base:.1f}% malicious\")\n", " print(out.to_string())\n", " return out\n", "\n", "_ = enrichment(real, \"REAL half\")\n", "print()\n", "_ = enrichment(synthetic, \"SYNTHETIC half\")" ] }, { "cell_type": "markdown", "id": "m41", "metadata": {}, "source": [ "#### Findings - outliers\n", "\n", "* **In the real data the outliers are the most class-informative rows in the table, and they point\n", " both ways.** Against a base rate of 55.4% malicious:\n", " * files with an extreme `js` count are **91.3% malicious** (+35.9 points) - heavy JavaScript is\n", " the attack itself;\n", " * files with an extreme `obj` count are only **5.1% malicious** (-50.3 points), and extreme\n", " `stream` only **8.1%** - these are large, genuine documents.\n", " * So the extremes at one end of the table are almost purely malicious and at the other end almost\n", " purely benign. **Deleting IQR outliers would delete the signal**, and it would do so\n", " asymmetrically, damaging the two classes by different amounts and biasing everything downstream.\n", "* **In the synthetic data the same outliers carry essentially no information.** Every feature lands\n", " between **78.9% and 82.9% malicious** against an 81.8% base rate - a swing of at most 1.1 points,\n", " where the real half swings by 86 points from end to end. The only mild exception is `js` at 97.3%.\n", "* That contrast is a **third independent confirmation of the domain gap** seen in Graphs 3-6: in the\n", " real corpus the tails of the distributions are where the classes separate, and in our synthetic\n", " corpus the tails are just big documents that happen to be big.\n", "* The extreme values are also **plausible, not corrupt**. The genuinely broken records were the\n", " extractor's crash rows, and those were dealt with in 1.4.2; a 23 MB PDF or a 595-page document is\n", " an unusual file, not a wrong measurement.\n", "\n", "#### Decision\n", "\n", "**We keep every outlier and change no values.** The extremes are real measurements that carry the\n", "class signal, so trimming, winsorizing or deleting them would remove exactly what the analysis\n", "depends on. What we do instead is handle them *presentationally and methodologically*:\n", "\n", "* every distribution plot in Step 2 uses a **log or symlog axis**, so the tails are visible without\n", " distorting the picture;\n", "* the **median** is quoted throughout rather than the mean, because the mean is not robust to these\n", " tails;\n", "* and for the modelling stage the recommendation is a **tree-based model**, which splits on rank\n", " order and is indifferent to how long a tail is. If a linear model is used instead, the count\n", " features should be `log1p`-transformed at that point - a modelling decision, not a data-cleaning\n", " one, so it is documented here and applied there." ] }, { "cell_type": "markdown", "id": "m42", "metadata": {}, "source": [ "---\n", "## Step 2 - Visual analysis\n", "\n", "### Graph 1 - Bar chart: how is the data composed?" ] }, { "cell_type": "code", "execution_count": 20, "id": "c43", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.492300Z", "iopub.status.busy": "2026-07-31T14:05:05.492019Z", "iopub.status.idle": "2026-07-31T14:05:05.729995Z", "shell.execute_reply": "2026-07-31T14:05:05.728537Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "label Benign Malicious\n", "source \n", "Real 44.6 55.4\n", "Synthetic 18.2 81.8\n" ] } ], "source": [ "counts = df.groupby([\"source\", \"label\"]).size().unstack(fill_value=0)\n", "\n", "ax = counts.plot(kind=\"bar\", color={\"Benign\": \"#4C78A8\", \"Malicious\": \"#E45756\"},\n", " edgecolor=\"black\", linewidth=.5)\n", "ax.set_title(\"Number of files by source and class\")\n", "ax.set_xlabel(\"\")\n", "ax.set_ylabel(\"Number of files\")\n", "ax.tick_params(axis=\"x\", rotation=0)\n", "ax.legend(title=\"label\")\n", "for container in ax.containers:\n", " ax.bar_label(container, padding=2, fontsize=9)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print((counts.T / counts.sum(axis=1) * 100).round(1).T)" ] }, { "cell_type": "markdown", "id": "m44", "metadata": {}, "source": [ "#### Findings - Graph 1\n", "\n", "* The dataset is **dominated by the real data**: 10,025 real files (90%) against 1,100 synthetic\n", " ones (10%). Any statistic computed on the pooled table is essentially a statistic about the real\n", " half; the synthetic half is almost invisible unless we split by `source`. Every graph from here on\n", " therefore separates the two.\n", "* **The class balance is very different in each half.** The real data is close to balanced -\n", " 55.4% Malicious / 44.6% Benign - which is comfortable for modelling. The synthetic data is\n", " deliberately skewed at 81.8% / 18.2%, because it was built for injection experiments rather than\n", " for training.\n", "* Because the real half is nearly balanced, **accuracy is an acceptable headline metric there**, but\n", " it would be misleading on the synthetic half where always predicting \"Malicious\" already scores\n", " 81.8%." ] }, { "cell_type": "markdown", "id": "m45", "metadata": {}, "source": [ "### Graph 2 - Histogram: do the two sources measure the same thing?" ] }, { "cell_type": "code", "execution_count": 21, "id": "c46", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:05.732825Z", "iopub.status.busy": "2026-07-31T14:05:05.732591Z", "iopub.status.idle": "2026-07-31T14:05:06.549745Z", "shell.execute_reply": "2026-07-31T14:05:06.548549Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Median by source (bytes):\n", " pdf_size metadata_size\n", "source \n", "Real 38912.0 268.0\n", "Synthetic 109148.0 NaN\n" ] } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "sizes = clean[\"pdf_size\"].dropna()\n", "bins = np.logspace(np.log10(max(sizes.min(), 1)), np.log10(sizes.max()), 50)\n", "\n", "for src, color in [(\"Real\", \"#4C78A8\"), (\"Synthetic\", \"#F58518\")]:\n", " ax.hist(clean.loc[clean[\"source\"] == src, \"pdf_size\"].dropna(),\n", " bins=bins, alpha=0.65, label=src, color=color, density=True)\n", "\n", "ax.set_xscale(\"log\")\n", "ax.set_xlabel(\"pdf_size (bytes, log scale)\")\n", "ax.set_ylabel(\"Density\")\n", "ax.set_title(\"File size after the unit fix: Real vs Synthetic\")\n", "ax.legend(title=\"source\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Median by source (bytes):\")\n", "print(clean.groupby(\"source\")[[\"pdf_size\", \"metadata_size\"]].median())" ] }, { "cell_type": "markdown", "id": "m47", "metadata": {}, "source": [ "#### Findings - Graph 2\n", "\n", "* This graph is the **verification of the unit fix from 1.4.1**. Before the fix the two distributions\n", " were two separated humps roughly 2,900x apart - an artefact of kilobytes versus bytes. Now they\n", " **overlap across most of their range**, which is what two corpora of ordinary PDF documents should\n", " look like.\n", "* A real difference does survive, and it is a believable one: the real files have a median of\n", " **38,912 bytes (38 KB)** against **109,148 bytes (107 KB)** for the synthetic files, about **2.8x**.\n", " We injected payloads into full-length documents, whereas the CIC corpus contains a large population\n", " of very small files. The synthetic corpus is therefore skewed towards bigger documents - worth\n", " remembering, but not a defect.\n", "* **`metadata_size` shows the contrast with a defect that could *not* be repaired.** `pdf_size` was a\n", " unit error, so rescaling fixed it. `metadata_size` was measuring a different quantity altogether on\n", " the synthetic side, so there was nothing to convert - it was voided to `` in 1.4.5, which is why\n", " its synthetic median now prints as `NaN` instead of the misleading `1`.\n", "* Note the log scale: on a linear axis this whole graph would be one spike at zero. Size data is\n", " almost always log-normal, which is also why the **median is the honest summary here, not the mean**\n", " (the mean is dragged upward by files of tens of megabytes).\n", "* From here on every graph shows the two halves side by side, so the real corpus and the synthetic\n", " corpus can be compared directly rather than described one after the other." ] }, { "cell_type": "markdown", "id": "m48", "metadata": {}, "source": [ "### Graph 3 - Boxplot: which features separate Malicious from Benign?\n", "\n", "The four features are drawn twice: the **real** files on the top row and the **synthetic** files on\n", "the bottom. Each column shares one y-axis, so the two panels of a feature are directly comparable." ] }, { "cell_type": "code", "execution_count": 22, "id": "c49", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:06.553211Z", "iopub.status.busy": "2026-07-31T14:05:06.552948Z", "iopub.status.idle": "2026-07-31T14:05:07.759801Z", "shell.execute_reply": "2026-07-31T14:05:07.758048Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Median by class, each half:\n", " obj stream pages xref_length\n", "source label \n", "Real Benign 47.0 19.0 2.0 55.5\n", " Malicious 10.0 2.0 1.0 16.0\n", "Synthetic Benign 32.5 12.0 2.0 466.0\n", " Malicious 35.0 12.0 2.0 487.0\n" ] } ], "source": [ "show = [\"obj\", \"stream\", \"pages\", \"xref_length\"]\n", "\n", "# Two rows: the same four features in each half of the data.\n", "# sharey=\"col\" puts both panels of a feature on one scale, so they can be compared by eye.\n", "fig, axes = plt.subplots(2, 4, figsize=(14, 9), sharey=\"col\")\n", "\n", "for row, src in enumerate([\"Real\", \"Synthetic\"]):\n", " sub = clean[clean[\"source\"] == src]\n", " for ax, col in zip(axes[row], show):\n", " sns.boxplot(data=sub, x=\"label\", y=col, order=[\"Benign\", \"Malicious\"],\n", " palette={\"Benign\": \"#4C78A8\", \"Malicious\": \"#E45756\"},\n", " hue=\"label\", legend=False, ax=ax, fliersize=1.5)\n", " ax.set_yscale(\"symlog\")\n", " ax.set_title(f\"{col}\" if row == 0 else \"\")\n", " ax.set_xlabel(\"\")\n", " ax.set_ylabel(\"\")\n", " axes[row][0].set_ylabel(f\"{src}\\n(n = {len(sub):,})\", fontsize=11)\n", "\n", "fig.suptitle(\"Structural features by class - Real (top) vs Synthetic (bottom), symlog scale\",\n", " y=1.00)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Median by class, each half:\")\n", "print(clean.groupby([\"source\", \"label\"])[show].median())" ] }, { "cell_type": "markdown", "id": "m50", "metadata": {}, "source": [ "#### Findings - Graph 3\n", "\n", "* **In the real data, malicious PDFs are structurally much simpler than benign ones.** The medians are\n", " striking: **47 objects vs 10**, **19 streams vs 2**, **2 pages vs 1**. The direction is the opposite\n", " of the intuitive guess - we expect an attacker to *add* content, but in practice a weaponised PDF is\n", " a nearly empty shell whose only purpose is to carry the payload, while a genuine document carries\n", " fonts, images and text.\n", "* The **boxes barely overlap** for `obj` and `stream` in the top row, so a single one of these\n", " features already carries real discriminative power - unlike anything we saw in the file-size graph.\n", "* **In the synthetic data the same four boxes are effectively identical.** Benign vs malicious medians\n", " are **32.5 vs 35** objects, **12 vs 12** streams, **2 vs 2** pages, **466 vs 487** xref entries. The\n", " quartiles overlap almost exactly too. Whatever separates the classes in the top row is simply absent\n", " from the bottom row.\n", "* The reason is visible in the panels themselves: our injection **did not change the host document's\n", " structure**. We started from ordinary PDFs and added a small payload, so the objects, streams and\n", " pages stayed where they were. Real malware is not built that way - it is assembled minimally from\n", " scratch, which is what produces the gap in the top row.\n", "* Note also that the two rows sit at **different absolute levels** for `xref_length` (real medians in\n", " the tens, synthetic in the hundreds). The synthetic corpus was built from larger, more complex\n", " source documents, which is consistent with the file-size difference seen in Graph 2.\n", "* All four features have **long tails and many outliers** in both halves, visible even on a symlog\n", " axis. Tree-based models handle this naturally; a linear model would need the features\n", " log-transformed first." ] }, { "cell_type": "markdown", "id": "m51", "metadata": {}, "source": [ "### Graph 4 - Heatmap: correlations between features and the target\n", "\n", "The same matrix is computed twice - once for each half of the data - so the two can be compared\n", "directly. Both panels use an identical colour scale, so a redder or bluer cell really does mean a\n", "stronger relationship." ] }, { "cell_type": "code", "execution_count": 23, "id": "c52", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:07.763043Z", "iopub.status.busy": "2026-07-31T14:05:07.762791Z", "iopub.status.idle": "2026-07-31T14:05:09.232674Z", "shell.execute_reply": "2026-07-31T14:05:09.230807Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "FEATURES = [\"pdf_size\", \"pages\", \"obj\", \"endobj\", \"stream\", \"endstream\", \"xref\", \"trailer\",\n", " \"startxref\", \"xref_length\", \"images\", \"js\", \"javascript\", \"open_action\",\n", " \"aa\", \"acroform\", \"keyword_embedded_file\", \"xfa\", \"launch\", \"objstm\"]\n", "\n", "corr_real = real[FEATURES + [\"is_malicious\"]].corr()\n", "corr_syn = synthetic[FEATURES + [\"is_malicious\"]].corr()\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(19, 8.5))\n", "mask = np.triu(np.ones_like(corr_real, dtype=bool))\n", "\n", "for ax, corr, name, n in [(axes[0], corr_real, \"Real\", len(real)),\n", " (axes[1], corr_syn, \"Synthetic\", len(synthetic))]:\n", " sns.heatmap(corr, mask=mask, cmap=\"coolwarm\", center=0, vmin=-1, vmax=1,\n", " annot=True, fmt=\".2f\", annot_kws={\"size\": 6}, square=True,\n", " linewidths=.4, cbar=False, ax=ax)\n", " ax.set_title(f\"{name} (n = {n:,})\")\n", "\n", "fig.suptitle(\"Correlation matrix, same features in each half of the data\", y=0.99)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "id": "c53", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:09.238087Z", "iopub.status.busy": "2026-07-31T14:05:09.237509Z", "iopub.status.idle": "2026-07-31T14:05:09.255502Z", "shell.execute_reply": "2026-07-31T14:05:09.254236Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Real Synthetic gap\n", "open_action 0.518 -0.066 0.584\n", "stream -0.388 0.009 0.397\n", "startxref -0.377 0.003 0.380\n", "obj -0.261 0.020 0.281\n", "acroform -0.253 -0.002 0.251\n", "xref -0.240 -0.030 0.210\n", "xfa 0.212 0.123 0.089\n", "objstm -0.175 0.014 0.189\n", "endstream -0.173 0.009 0.182\n", "trailer -0.170 -0.030 0.140\n", "endobj -0.144 0.020 0.164\n", "xref_length 0.129 -0.000 0.129\n", "pages -0.119 0.023 0.142\n", "javascript 0.115 0.090 0.025\n", "images -0.114 0.011 0.125\n", "launch 0.102 0.132 0.030\n", "js 0.084 0.085 0.001\n", "aa -0.048 -0.042 0.006\n", "pdf_size -0.038 -0.045 0.007\n", "keyword_embedded_file -0.021 0.167 0.188\n", "\n", "Strongest |correlation| with the target:\n", " Real : 0.518\n", " Synthetic : 0.167\n" ] } ], "source": [ "# Side-by-side comparison of the correlation with the target\n", "compare = pd.DataFrame({\n", " \"Real\": corr_real[\"is_malicious\"].drop(\"is_malicious\"),\n", " \"Synthetic\": corr_syn[\"is_malicious\"].drop(\"is_malicious\"),\n", "}).round(3)\n", "compare[\"gap\"] = (compare[\"Real\"] - compare[\"Synthetic\"]).abs().round(3)\n", "print(compare.sort_values(\"Real\", key=abs, ascending=False).to_string())\n", "\n", "print(\"\\nStrongest |correlation| with the target:\")\n", "print(f\" Real : {compare['Real'].abs().max():.3f}\")\n", "print(f\" Synthetic : {compare['Synthetic'].abs().max():.3f}\")" ] }, { "cell_type": "markdown", "id": "m54", "metadata": {}, "source": [ "#### Findings - Graph 4\n", "\n", "* **The real panel contains genuine signal.** Several features correlate usefully with the target:\n", " `open_action` **+0.52**, `stream` **-0.39**, `startxref` **-0.38**, `obj` **-0.26**,\n", " `acroform` **-0.25**, `xref` **-0.24**. For messy security data these are respectable.\n", "* The **signs tell a coherent story**. `open_action` (an instruction that runs something the moment\n", " the file is opened) is positively associated with malice, while everything that measures *document\n", " richness* - streams, objects, cross-reference entries - is negatively associated. This confirms\n", " Graph 3 numerically: malicious = simple structure + one automatic trigger.\n", "* **The synthetic panel is almost blank in the target row, and that is the headline.** Its strongest\n", " correlation with `is_malicious` is `keyword_embedded_file` at **0.167** - weaker than the *sixth*\n", " strongest feature on the real side. Nothing in the synthetic half comes close to a usable\n", " relationship.\n", "* **The rankings do not merely weaken, they disagree.** `open_action` is the best real predictor at\n", " +0.52 but is **-0.07 in the synthetic half - the sign is reversed**. `stream` goes from -0.39 to\n", " +0.01, `startxref` from -0.38 to +0.00. Meanwhile the synthetic top three -\n", " `keyword_embedded_file` (0.17), `launch` (0.13), `xfa` (0.12) - are simply the payload types our\n", " generator happened to inject most often. The synthetic half is not a weaker version of the real\n", " signal; it is a **different signal entirely**, and it reflects our generator rather than malware.\n", "* **Why the difference is structural, not statistical.** In the synthetic half most flag columns are\n", " nearly constant - `launch` and `xfa` take only 2 distinct values, `open_action` 3. We injected into\n", " ordinary documents, so the host structure barely moved, and a feature that hardly varies cannot\n", " correlate with anything.\n", "* Within the feature block, some pairs are **almost perfectly collinear in both panels**: `obj` with\n", " `endobj` and `stream` with `endstream` (r > 0.95). That is expected - a well-formed PDF closes every\n", " object it opens - so these pairs are duplicate information and only one of each should enter a\n", " model. Interestingly, the *gap* between them (an object opened but never closed) is a corruption\n", " signal a future feature could capture." ] }, { "cell_type": "markdown", "id": "m55", "metadata": {}, "source": [ "### Graph 5 - Scatter plot: does the synthetic data behave like the real data?" ] }, { "cell_type": "code", "execution_count": 25, "id": "c56", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:09.258660Z", "iopub.status.busy": "2026-07-31T14:05:09.258422Z", "iopub.status.idle": "2026-07-31T14:05:09.801541Z", "shell.execute_reply": "2026-07-31T14:05:09.799881Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Share of files containing each flag, by group:\n", "grp Real - Benign Real - Malicious Synthetic - Benign Synthetic - Malicious\n", "js 6.4 71.8 2.5 19.8\n", "javascript 6.6 73.4 1.0 18.7\n", "open_action 4.9 51.4 24.5 17.9\n", "keyword_embedded_file 8.3 8.1 1.0 26.4\n", "launch 0.1 2.3 0.0 8.9\n", "xfa 0.0 9.2 0.0 7.8\n" ] } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(13, 5.5), sharex=True, sharey=True)\n", "\n", "for ax, src in zip(axes, [\"Real\", \"Synthetic\"]):\n", " sub = clean[clean[\"source\"] == src]\n", " for cls, color in [(\"Benign\", \"#4C78A8\"), (\"Malicious\", \"#E45756\")]:\n", " s = sub[sub[\"label\"] == cls]\n", " ax.scatter(s[\"obj\"], s[\"stream\"], s=12, alpha=0.35, c=color, label=cls,\n", " edgecolors=\"none\")\n", " ax.set_xscale(\"symlog\")\n", " ax.set_yscale(\"symlog\")\n", " ax.set_xlabel(\"obj (number of PDF objects)\")\n", " ax.set_title(f\"{src} (n = {len(sub):,})\")\n", "\n", "axes[0].set_ylabel(\"stream (number of streams)\")\n", "axes[0].legend(title=\"label\", markerscale=2)\n", "fig.suptitle(\"Objects vs streams: the same two features in each half of the data\", y=1.00)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Share of files containing each flag, by group:\")\n", "flags = [\"js\", \"javascript\", \"open_action\", \"keyword_embedded_file\", \"launch\", \"xfa\"]\n", "print((clean.assign(grp=clean[\"source\"] + \" - \" + clean[\"label\"])\n", " .groupby(\"grp\")[flags].apply(lambda g: (g > 0).mean()) * 100).round(1).T)" ] }, { "cell_type": "markdown", "id": "m57", "metadata": {}, "source": [ "#### Findings - Graph 5\n", "\n", "* **In the real panel the two classes form two visibly different clouds**: benign files spread up and\n", " to the right (many objects, many streams), malicious files bunch into the bottom-left corner. The\n", " separation found in Graphs 3 and 4 is visible directly in the raw points.\n", "* **In the synthetic panel the two clouds sit on top of each other.** Our injected files are\n", " indistinguishable from our clean files on exactly the features that work best on real data. This\n", " makes sense: we injected payloads into *normal* documents, so the host document's structure barely\n", " changed - whereas real malware authors build a minimal file from scratch.\n", "* The flag table makes the gap explicit. `js` is present in **71.8% of real malicious** files but only\n", " **19.8% of synthetic malicious** ones; `open_action` in **51.4%** vs **17.9%**. Conversely `launch`\n", " and `keyword_embedded_file` are *more* common in our synthetic attacks (8.9% and 26.4%) than in real\n", " malware (2.3% and 8.1%), because those are the payload types our generator favoured.\n", "* **The key conclusion of this EDA:** the synthetic corpus is **not a substitute for real malicious\n", " PDFs** in the CIC feature space. A detector tuned to the real half should not be expected to carry\n", " over to the synthetic half, and the two halves should never be merged and treated as one\n", " population. The synthetic corpus is still valuable - as a *benchmark* for testing AI detectors -\n", " but the label \"malicious\" means structurally different things in the two halves." ] }, { "cell_type": "markdown", "id": "m58", "metadata": {}, "source": [ "### Graph 6 - Ranked comparison: how every feature correlates with the target\n", "\n", "Graphs 3-5 compared the two halves one aspect at a time. This last view puts the whole picture in one\n", "place: the correlation of **every** feature with `is_malicious`, ranked, for the real and the\n", "synthetic corpus side by side." ] }, { "cell_type": "code", "execution_count": 26, "id": "c59", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:09.805029Z", "iopub.status.busy": "2026-07-31T14:05:09.804756Z", "iopub.status.idle": "2026-07-31T14:05:09.825500Z", "shell.execute_reply": "2026-07-31T14:05:09.824077Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correlation with is_malicious, ranked by strength in the REAL data\n", "==============================================================================\n", " real rank_real synthetic rank_synthetic\n", "open_action 0.518 1 -0.066 6\n", "stream -0.388 2 0.009 17\n", "startxref -0.377 3 0.003 18\n", "obj -0.261 4 0.020 13\n", "acroform -0.253 5 -0.002 19\n", "xref -0.240 6 -0.030 9\n", "xfa 0.212 7 0.123 3\n", "objstm -0.175 8 0.014 14\n", "endstream -0.173 9 0.009 16\n", "trailer -0.170 10 -0.030 10\n", "endobj -0.144 11 0.020 12\n", "xref_length 0.129 12 -0.000 20\n", "pages -0.119 13 0.023 11\n", "javascript 0.115 14 0.090 4\n", "images -0.114 15 0.011 15\n", "launch 0.102 16 0.132 2\n", "js 0.084 17 0.085 5\n", "aa -0.048 18 -0.042 8\n", "pdf_size -0.038 19 -0.045 7\n", "keyword_embedded_file -0.021 20 0.167 1\n", "\n", "\n", "Top 5 in each half (they barely overlap):\n", " Real : ['open_action', 'stream', 'startxref', 'obj', 'acroform']\n", " Synthetic : ['keyword_embedded_file', 'launch', 'xfa', 'javascript', 'js']\n" ] } ], "source": [ "ranked = pd.DataFrame({\n", " \"real\": corr_real[\"is_malicious\"].drop(\"is_malicious\"),\n", " \"synthetic\": corr_syn[\"is_malicious\"].drop(\"is_malicious\"),\n", "})\n", "ranked[\"abs_real\"] = ranked[\"real\"].abs()\n", "ranked[\"abs_synthetic\"] = ranked[\"synthetic\"].abs()\n", "\n", "# Rank within each half separately (1 = strongest relationship in that corpus)\n", "ranked[\"rank_real\"] = ranked[\"abs_real\"].rank(ascending=False).astype(int)\n", "ranked[\"rank_synthetic\"] = ranked[\"abs_synthetic\"].rank(ascending=False).astype(int)\n", "\n", "ranked = ranked.sort_values(\"abs_real\", ascending=False)\n", "\n", "print(\"Correlation with is_malicious, ranked by strength in the REAL data\")\n", "print(\"=\" * 78)\n", "print(ranked[[\"real\", \"rank_real\", \"synthetic\", \"rank_synthetic\"]].round(3).to_string())\n", "\n", "print(\"\\n\\nTop 5 in each half (they barely overlap):\")\n", "print(\" Real :\", list(ranked.sort_values(\"abs_real\", ascending=False).index[:5]))\n", "print(\" Synthetic :\", list(ranked.sort_values(\"abs_synthetic\", ascending=False).index[:5]))" ] }, { "cell_type": "code", "execution_count": 27, "id": "c60", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:09.829134Z", "iopub.status.busy": "2026-07-31T14:05:09.828695Z", "iopub.status.idle": "2026-07-31T14:05:10.117849Z", "shell.execute_reply": "2026-07-31T14:05:10.116875Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "order = ranked.index # already sorted by strength in the real data\n", "x = np.arange(len(order))\n", "width = 0.4\n", "\n", "fig, ax = plt.subplots(figsize=(14, 6.5))\n", "ax.bar(x - width/2, ranked[\"real\"], width, label=\"Real\", color=\"#4C78A8\")\n", "ax.bar(x + width/2, ranked[\"synthetic\"], width, label=\"Synthetic\", color=\"#F58518\")\n", "\n", "ax.axhline(0, color=\"black\", linewidth=.8)\n", "ax.set_xticks(x)\n", "ax.set_xticklabels(order, rotation=45, ha=\"right\") # tilted so the names do not overlap\n", "ax.set_ylabel(\"Correlation with is_malicious\")\n", "ax.set_title(\"Feature correlation with the target, ranked by strength in the real data\")\n", "ax.legend(title=\"source\")\n", "ax.grid(axis=\"x\", visible=False)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "m61", "metadata": {}, "source": [ "#### Findings - Graph 6\n", "\n", "* **The blue bars form a clear staircase; the orange bars are nearly flat.** Reading left to right,\n", " the real correlations fall from **0.52** to about 0.02, giving a usable ordering of features. The\n", " synthetic bars hug the zero line across the entire chart - its single largest value is **0.167**,\n", " which would rank only **11th of 20** among the real features.\n", "* **The two rankings disagree at the very top.** The five strongest features in each half barely\n", " overlap: real leads with `open_action`, `stream`, `startxref`, `obj`, `acroform`, while synthetic\n", " leads with `keyword_embedded_file`, `launch`, `xfa`, `javascript`, `js`. A model would learn a\n", " different set of rules from each corpus.\n", "* **`open_action` is the clearest single illustration.** Its blue bar is the tallest on the chart at\n", " **+0.52**; its orange bar points the *other way* at **-0.07**. A feature that is the best available\n", " predictor in one corpus and slightly counter-productive in the other is the strongest possible\n", " evidence that the two corpora do not share a data-generating process.\n", "* **Where the two do agree, the features are weak.** `js` (0.084 vs 0.085), `aa` (-0.048 vs -0.042)\n", " and `pdf_size` (-0.038 vs -0.045) line up almost exactly - but all three are close to zero in both\n", " halves, so the agreement is agreement about *noise*, not shared signal.\n", "* This chart is the compact statement of the notebook's main result, and it is worth reading together\n", " with Graph 5: correlation understates the rare-but-precise flags, yet even on the flags' own terms\n", " the two corpora rank them differently." ] }, { "cell_type": "markdown", "id": "m62", "metadata": {}, "source": [ "### Graph 7 - UMAP: the final test of similarity\n", "\n", "Graphs 1-6 each compared the two corpora **one feature, or one pair of features, at a time**. Every\n", "one of them found a difference, but each was a partial view: a boxplot sees four features, a\n", "correlation matrix sees pairs. It is still possible in principle for two datasets to differ on every\n", "individual feature yet occupy the same region of the overall feature space.\n", "\n", "**UMAP settles that question, and is therefore the final test of similarity in Part 1.** It takes all\n", "29 features at once and lays every file out on a two-dimensional map, positioning files that are\n", "similar *across the whole feature vector* close together. We then colour each point by which corpus\n", "it came from - **red for real, blue for synthetic** - and simply look.\n", "\n", "The test is easy to read:\n", "\n", "* if the two corpora describe the same kind of object, **the colours will be mixed throughout** -\n", " synthetic files will land among real files of similar structure;\n", "* if they describe different kinds of object, **the colours will form separate territories**.\n", "\n", "Nothing about the target variable is used here. The map is built from the structural features alone,\n", "so any separation we see is a statement about the *files*, not about our labels.\n", "\n", "#### The embedding step\n", "\n", "UMAP cannot consume the cleaned table directly. Three preparations are needed first, and each is a\n", "decision worth stating:\n", "\n", "1. **Drop `metadata_size`.** It is `` for every synthetic row (1.4.5), so it carries no\n", " information for half the map and would have to be invented. That leaves **29 features**.\n", "2. **Fill the remaining gaps with the column median.** About 4.7% of the matrix is still missing,\n", " mostly from the sentinels converted in 1.5. UMAP requires a complete matrix; the median is the\n", " conservative filler for skewed counts, and it pulls points *towards* the centre - meaning it makes\n", " the two corpora look **more** alike, not less. Any separation that survives it is real.\n", "3. **`log1p`-transform, then standardise.** UMAP measures distance, so raw counts would let\n", " `xref_length` (max 589,853) drown out `launch` (max 2) entirely. `log1p` compresses the long tails\n", " documented in 1.6 without discarding the outliers, and `StandardScaler` then puts every feature on\n", " a comparable scale so that each contributes to the distance on equal terms." ] }, { "cell_type": "code", "execution_count": 28, "id": "c63", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:10.120960Z", "iopub.status.busy": "2026-07-31T14:05:10.120736Z", "iopub.status.idle": "2026-07-31T14:05:23.173813Z", "shell.execute_reply": "2026-07-31T14:05:23.172259Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Python314\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Feature matrix : (11125, 29)\n", "Still missing : 4.74%\n", "Prepared matrix: (11125, 29) | mean ~-0.00 | sd ~1.00\n" ] } ], "source": [ "# Requires umap-learn: pip install umap-learn\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", message=\".*n_jobs value.*\")\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.neighbors import NearestNeighbors\n", "import umap\n", "\n", "# 1. Feature matrix: every measurement column except metadata_size (all-NA for synthetic)\n", "UMAP_FEATURES = [c for c in clean.columns\n", " if c not in [\"label\", \"source\", \"is_malicious\", \"metadata_size\"]]\n", "\n", "X = clean[UMAP_FEATURES].astype(\"float64\")\n", "print(\"Feature matrix :\", X.shape)\n", "print(\"Still missing : {:.2f}%\".format(X.isna().mean().mean() * 100))\n", "\n", "# 2. Median impute, 3. log1p, then standardise\n", "X_filled = X.fillna(X.median())\n", "X_log = np.log1p(X_filled.clip(lower=0))\n", "X_scaled = StandardScaler().fit_transform(X_log)\n", "\n", "print(\"Prepared matrix:\", X_scaled.shape,\n", " \"| mean ~{:.2f} | sd ~{:.2f}\".format(X_scaled.mean(), X_scaled.std()))" ] }, { "cell_type": "code", "execution_count": 29, "id": "c64", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:05:23.181165Z", "iopub.status.busy": "2026-07-31T14:05:23.179731Z", "iopub.status.idle": "2026-07-31T14:06:02.770719Z", "shell.execute_reply": "2026-07-31T14:06:02.769397Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Build the 2-D map. random_state fixes the layout so the figure is reproducible.\n", "reducer = umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2, random_state=SEED)\n", "embedding = reducer.fit_transform(X_scaled)\n", "\n", "proj = pd.DataFrame(embedding, columns=[\"UMAP-1\", \"UMAP-2\"], index=clean.index)\n", "proj[\"source\"] = clean[\"source\"].values\n", "\n", "fig, ax = plt.subplots(figsize=(10, 8.5))\n", "for src, color in [(\"Real\", \"#E45756\"), (\"Synthetic\", \"#4C78A8\")]:\n", " pts = proj[proj[\"source\"] == src]\n", " ax.scatter(pts[\"UMAP-1\"], pts[\"UMAP-2\"], s=6, alpha=0.35, c=color,\n", " label=f\"{src} (n = {len(pts):,})\", edgecolors=\"none\")\n", "\n", "ax.set_xlabel(\"UMAP-1\")\n", "ax.set_ylabel(\"UMAP-2\")\n", "ax.set_title(f\"UMAP of all {len(proj):,} files over {len(UMAP_FEATURES)} structural features\\n\"\n", " \"red = real corpus, blue = synthetic corpus\")\n", "ax.legend(markerscale=3, loc=\"best\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "id": "c65", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:06:02.773572Z", "iopub.status.busy": "2026-07-31T14:06:02.773267Z", "iopub.status.idle": "2026-07-31T14:06:02.862161Z", "shell.execute_reply": "2026-07-31T14:06:02.859882Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " % of the dataset mean % of neighbours same source % with NO neighbour from other corpus\n", "source \n", "Real 90.1 97.8 86.7\n", "Synthetic 9.9 77.9 39.1\n", "\n", "If the two corpora were interchangeable, the middle column would match the first.\n" ] } ], "source": [ "# Quantify what the picture shows: of each file's 20 nearest neighbours on the map,\n", "# how many come from its own corpus?\n", "nn = NearestNeighbors(n_neighbors=21).fit(proj[[\"UMAP-1\", \"UMAP-2\"]])\n", "_, idx = nn.kneighbors(proj[[\"UMAP-1\", \"UMAP-2\"]])\n", "\n", "src = proj[\"source\"].to_numpy()\n", "same_source = (src[idx[:, 1:]] == src[:, None]).mean(axis=1) # drop self-match\n", "proj[\"same_source_share\"] = same_source\n", "\n", "share_of_data = proj[\"source\"].value_counts(normalize=True) * 100\n", "\n", "summary = pd.DataFrame({\n", " \"% of the dataset\": share_of_data.round(1),\n", " \"mean % of neighbours same source\": (proj.groupby(\"source\")[\"same_source_share\"].mean() * 100).round(1),\n", " \"% with NO neighbour from other corpus\":\n", " (proj.groupby(\"source\")[\"same_source_share\"].apply(lambda s: (s == 1).mean()) * 100).round(1),\n", "})\n", "print(summary.to_string())\n", "print(\"\\nIf the two corpora were interchangeable, the middle column would match the first.\")" ] }, { "cell_type": "markdown", "id": "m66", "metadata": {}, "source": [ "#### Findings - Graph 7\n", "\n", "* **The relationship is containment, not overlap.** The red corpus spreads across the entire map in\n", " dozens of thin filaments - real PDFs are enormously varied. The blue corpus is confined to a single\n", " compact band in the upper-centre, roughly a fifth of the occupied area. **Every blue point sits\n", " inside the red footprint, but blue reaches almost none of it.** The whole lower and outer half of\n", " the map contains essentially no synthetic files at all.\n", "* **Within that band, blue still forms its own streaks.** Zooming into the shared region, the\n", " synthetic points do not blend into the red filaments - they form their own parallel arcs. The\n", " neighbour statistic quantifies it: the synthetic corpus is **9.9%** of the data, so if the two were\n", " interchangeable a synthetic file's neighbours would be about 9.9% synthetic. Instead **77.9%** are,\n", " an eightfold enrichment, and **39.1% of synthetic files have no real file at all** among their 20\n", " nearest neighbours.\n", "* **The real figure of 97.8% is the less interesting one** and should not be over-read: real files\n", " make up 90.1% of the data, so a high same-source rate is close to what mere abundance would give.\n", " The synthetic 77.9%-against-9.9% is where the evidence actually lies.\n", "* **The separation is not an artefact of our preparation.** Median imputation pulls points toward the\n", " centre of the cloud and `log1p` compresses the very tails where the corpora differ most - both\n", " choices work *against* finding a difference. The structure survives them anyway.\n", "* **Nor is it an artefact of the target.** No label was used in building the map. UMAP saw only\n", " structural measurements and still organised the two corpora differently, which means the files are\n", " distinguishable as *documents*, before any question of malice arises.\n", "* **What this adds to Graphs 1-6.** Those showed the corpora differ feature by feature; Graph 7 shows\n", " what that amounts to for whole files. Our 1,100 synthetic PDFs are a **narrow, homogeneous island**\n", " drawn from one small neighbourhood of a very diverse real population - unsurprising, since they were\n", " all built by one script from a limited pool of source documents. A model fitted on the real corpus\n", " is therefore tested, in Part 2, on a thin slice of the space it learned; and a model fitted on the\n", " synthetic corpus would be extrapolating almost everywhere. Transfer between the halves cannot be\n", " assumed - it has to be measured." ] }, { "cell_type": "markdown", "id": "m67", "metadata": {}, "source": [ "---\n", "## Step 3 - Summary\n", "\n", "1. **The column names had to be repaired first.** A typo (`Fine name`), spaces inside names, three\n", " competing naming conventions, and a near-identical pair (`embedded files` / `EmbeddedFile`) that\n", " could have been confused silently. All renamed to `snake_case` before any analysis.\n", "\n", "2. **Five concrete repairs were needed before any analysis** (1.4): `pdf_size` converted to a single\n", " unit (bytes); 147 cells of extractor crash debris given their own `-2` sentinel; `has_text`\n", " encoded from five mixed value types into a nullable `Int64`; and five count columns parsed out of\n", " text across every measurement column, recovering all 638 counts hidden behind the `n(m)` notation;\n", " and `metadata_size` voided for the synthetic half, where it was not a byte size at all. No rows and\n", " no columns were lost.\n", "\n", "3. **The data hides its own missing values.** 15,016 cells carry a sentinel instead of a\n", " measurement, across 30 of the 31 feature columns. Worse, they are concentrated in\n", " malicious files (27.2% of real malicious rows vs 0.8% of benign), so the extraction failure is\n", " itself correlated with the target and must be handled deliberately rather than exploited.\n", "\n", "4. **The two sources were not on the same scale.** `pdf_size` was in kilobytes for the real data and\n", " bytes for the synthetic data - a ~3,000x artefact, fixed by rescaling. `metadata_size` was worse:\n", " on the synthetic side it was not a byte size at all, so it was voided to `` rather than\n", " repaired. Columns must be verified to mean the same thing before two datasets are stacked\n", " together.\n", "\n", "5. **The outliers are the signal, so none were removed** (1.6). The textbook IQR rule flags 4-19% of\n", " rows, but in the real half those rows are the most class-informative in the table and they point\n", " both ways: files with extreme `js` are **91.3% malicious**, files with extreme `obj` only\n", " **5.1%**, against a 55.4% base rate. Deleting them would have destroyed the signal asymmetrically.\n", " The rule also breaks on the zero-inflated columns, where `Q3 + 1.5 x IQR` evaluates to 0 and\n", " declares every non-zero value an outlier.\n", "\n", "6. **Malicious PDFs are simpler, not bigger.** Fewer objects, fewer streams, fewer pages, plus an\n", " automatic `open_action` trigger. This holds consistently across the boxplots, the correlation\n", " matrix and the scatter plot.\n", "\n", "7. **The real half contains usable signal** - `open_action` (r = +0.52), `stream` (-0.39) and\n", " `startxref` (-0.38) - and the real half is nearly class-balanced, so it is the sound basis for a\n", " detector.\n", "\n", "8. **The synthetic half does not reproduce that signal.** This is the central result, and four\n", " independent lines of evidence agree on it:\n", " * **Distributions** (Graph 3): the four features that separate the classes in the real data have\n", " near-identical medians by class in the synthetic data - 32.5 vs 35 objects, 12 vs 12 streams.\n", " * **Correlations** (Graphs 4 and 6): the strongest synthetic correlation is 0.167, which would\n", " rank only 11th of 20 on the real side; and the rankings *disagree* rather than merely weaken -\n", " `open_action` is the best real predictor at +0.52 but **-0.07**, sign reversed, on the synthetic\n", " side.\n", " * **Outliers** (1.6): real outliers swing 86 points across features, synthetic outliers stay\n", " within 3 points of their base rate - the tails carry no class information at all.\n", " * **The whole feature space** (Graph 7): UMAP, using no labels, places the synthetic corpus in a\n", " compact island. **77.9%** of a synthetic file's nearest neighbours are synthetic where mere\n", " abundance predicts 9.9%, and 39.1% have no real neighbour whatsoever.\n", "\n", "9. **The synthetic corpus is a narrow slice, not a small copy** (Graph 7). Every synthetic point lies\n", " inside the real footprint, but the synthetic files reach only about a fifth of the occupied map -\n", " one script working from a limited pool of source documents cannot reproduce the diversity of\n", " 10,025 real PDFs. This is a limit on what the benchmark can claim, and it should be stated\n", " whenever results from it are reported." ] }, { "cell_type": "markdown", "id": "m68", "metadata": {}, "source": [ "---\n", "## Step 4 - Conclusion: what the two corpora are each good for\n", "\n", "Part 2 takes **existing open-source AI models** and evaluates their ability to tell our **injected**\n", "PDFs from **clean** ones. Nothing is trained. Everything above bears on how that evaluation should be\n", "read, so it is worth stating the conclusion explicitly.\n", "\n", "### Two corpora, two different notions of \"malicious\"\n", "\n", "The EDA showed repeatedly that the two corpora do not encode the same concept. A detector built\n", "around either one - whether trained on it, or hand-written from the flags it emphasises, as classical\n", "antivirus rules are - would key on different features, in different directions, and would be\n", "measuring a different thing. The comparison below is what each corpus *teaches*, and it explains why\n", "a scanner tuned to the real corpus can walk straight past our files.\n", "\n", "| | Built around **real** (CIC) malware | Built around **synthetic** injected PDFs |\n", "|---|---|---|\n", "| What it keys on | Global document *shape* | Presence of a specific injected artefact |\n", "| Top features (Graph 6) | `open_action` +0.52, `stream` -0.39, `startxref` -0.38, `obj` -0.26 | `keyword_embedded_file` 0.17, `launch` 0.13, `xfa` 0.12 |\n", "| Strength of signal | Strong - correlations up to 0.52 | Weak - nothing above 0.17 |\n", "| Implicit rule | \"Malicious files are **small and structurally simple**, with one automatic trigger\" | \"Malicious files contain **one extra object** that clean files do not\" |\n", "| What the outliers mean (1.6) | The tails carry the class: extreme `js` 91% malicious, extreme `obj` 5% | The tails carry nothing: every feature within 3 points of the base rate |\n", "| Region of feature space (Graph 7) | Spread across the whole UMAP map in dozens of filaments | A single compact island, ~1/5 of the occupied area |\n", "| Failure mode | Flags any minimal PDF - a one-page form looks like an attack | Flags any file carrying an embedded object - a legitimate attachment looks like an attack |\n", "\n", "The clearest single illustration is `open_action`: the **best** predictor on real data at +0.52, and\n", "**-0.07** on synthetic data. A rule taken from one corpus is not merely weaker on the other, it\n", "points the wrong way.\n", "\n", "The reason is not statistical but procedural. Real malware is *authored* - built minimally from\n", "scratch to carry a payload, which is why real malicious files are structurally impoverished. Our\n", "synthetic files were *injected* - we took ordinary, complete documents and added a payload to them,\n", "so the host document's structure barely moved (Graph 3: 32.5 vs 35 objects, 12 vs 12 streams). The\n", "two corpora encode two different threat models.\n", "\n", "**Graph 7 is the compact statement of all of this.** Given all 29 features at once and no labels at\n", "all, UMAP still sorts the two corpora apart: 77.9% of a synthetic file's neighbours are synthetic\n", "where abundance alone predicts 9.9%. The corpora are distinguishable as *documents*, before the\n", "question of malice is even asked - which is why the difference between the two detectors above is\n", "structural rather than a matter of tuning.\n", "\n", "### Why the synthetic corpus is the right thing to evaluate on\n", "\n", "That difference is usually described as a limitation. For the evaluation stage it is the **entire\n", "point**, for three reasons.\n", "\n", "**1. The synthetic payloads are invisible to the CIC feature vocabulary.** We count how many of the\n", "CIC \"suspicious keyword\" flags each file raises - the check a flag-based scanner would perform:" ] }, { "cell_type": "code", "execution_count": 31, "id": "c69", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:06:02.865531Z", "iopub.status.busy": "2026-07-31T14:06:02.865294Z", "iopub.status.idle": "2026-07-31T14:06:02.884528Z", "shell.execute_reply": "2026-07-31T14:06:02.883208Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " mean flags raised % raising ZERO flags\n", "source label \n", "Real Benign 0.3 77.3\n", " Malicious 2.2 16.6\n", "Synthetic Benign 0.3 69.5\n", " Malicious 1.0 37.3\n" ] } ], "source": [ "CIC_FLAGS = [\"js\", \"javascript\", \"aa\", \"open_action\", \"launch\",\n", " \"keyword_embedded_file\", \"xfa\", \"jbig2decode\", \"rich_media\"]\n", "\n", "flags_raised = (clean[CIC_FLAGS].fillna(0) > 0).sum(axis=1)\n", "\n", "evasion = pd.DataFrame({\n", " \"mean flags raised\": flags_raised.groupby([clean[\"source\"], clean[\"label\"]]).mean(),\n", " \"% raising ZERO flags\": flags_raised.eq(0).groupby([clean[\"source\"], clean[\"label\"]]).mean() * 100,\n", "}).round(1)\n", "\n", "print(evasion.to_string())" ] }, { "cell_type": "markdown", "id": "m70", "metadata": {}, "source": [ "| | mean flags raised | share raising **zero** flags |\n", "|---|---|---|\n", "| Real - Malicious | 2.2 | 16.6% |\n", "| Synthetic - Malicious | 1.0 | **37.3%** |\n", "| Real - Benign | 0.3 | 77.3% |\n", "| Synthetic - Benign | 0.3 | 69.5% |\n", "\n", "A real malicious file raises **more than twice as many** flags as one of ours, and where only 16.6%\n", "of real malware stays completely silent, **37.3% of our injected files raise no CIC flag at all** -\n", "more than double the evasion rate. They are, by the standards of\n", "a detector built on this feature set, indistinguishable from clean documents. A signature or\n", "flag-counting antivirus of the kind the CIC features describe would pass them straight through.\n", "\n", "**2. They are nonetheless genuinely dangerous.** The manifest records that every payload is drawn\n", "from a standard malware **test corpus** - EICAR (221 files), WICAR (255), AMTSO (215) and RANSIM\n", "(209). These are deliberately harmless stand-ins, but they are the exact strings and structures the\n", "security industry uses to verify that a scanner *works*. A file carrying one is functionally an\n", "attack in every respect except the damage it does.\n", "\n", "**3. Real scanners do detect them - which proves the payloads are live.** While transferring the\n", "generated corpus from Google Drive, **every `object_action_injection` file (80 of them) was blocked\n", "by Google's malware scanner**. That is an independent, third-party confirmation from a production\n", "security system: these files are not inert props. `object_action_injection` is the family that pairs\n", "a `/Launch` action with an embedded object - `launch` = 1.0 and `keyword_embedded_file` = 1.0 in\n", "every one of those files (Graph 5) - and it is the family most heavily weighted towards AMTSO\n", "payloads (42 of 80).\n", "\n", "So we have a corpus that is **provably malicious to a production scanner** while remaining **largely\n", "invisible to the structural feature set** the academic benchmark is built on. That gap is precisely\n", "what makes it a useful test.\n", "\n", "### Does the corpus have enough internal variety to test a model properly?\n", "\n", "Part 2 trains nothing. It takes **existing open-source AI models off the shelf** and asks each one a\n", "security question: can you tell an injected PDF from a clean one? For that to be a fair test of a\n", "model's security capability rather than a trick, the corpus has to be **internally varied** - if all\n", "900 malicious files were the same attack wearing different filenames, a model could score 100% by\n", "recognising one artefact, and we would have learned nothing about its security reasoning.\n", "\n", "Graph 7 showed the corpus is narrow *in one specific sense*: its **host documents** occupy a small\n", "region of PDF space. That is a statement about the carriers, not about the attacks. Along the\n", "dimension that the evaluation actually probes - what was injected and how - the corpus is deliberately\n", "and measurably diverse. The manifest records that design." ] }, { "cell_type": "code", "execution_count": 32, "id": "c71", "metadata": { "execution": { "iopub.execute_input": "2026-07-31T14:06:02.888500Z", "iopub.status.busy": "2026-07-31T14:06:02.887933Z", "iopub.status.idle": "2026-07-31T14:06:02.903236Z", "shell.execute_reply": "2026-07-31T14:06:02.902013Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "attack families (injection_type) : 12\n", "source frameworks : 9\n", "base payload families : 4\n", "insertion strategies : 3\n", "obfuscation strategies : 6\n", "distinct payload variants : 550\n", "distinct host documents : 1045\n", "\n", "Distinct attack configurations among the 900 injected files: 337\n" ] } ], "source": [ "design = {\n", " \"attack families (injection_type)\": manifest[\"injection_type\"].nunique() - 1, # minus 'none'\n", " \"source frameworks\": manifest[\"framework\"].nunique() - 1,\n", " \"base payload families\": manifest[\"base_payload\"].nunique() - 1,\n", " \"insertion strategies\": manifest[\"insertion_strategy\"].nunique() - 1,\n", " \"obfuscation strategies\": manifest[\"obfuscation_strategy\"].nunique() - 1, # minus 'none'\n", " \"distinct payload variants\": manifest[\"variant_used\"].nunique(),\n", " \"distinct host documents\": manifest[\"source_file\"].nunique(),\n", "}\n", "for k, v in design.items():\n", " print(f\"{k:34s}: {v}\")\n", "\n", "injected = manifest[manifest[\"injection_type\"] != \"none\"]\n", "combos = injected.drop_duplicates([\"injection_type\", \"framework\", \"base_payload\",\n", " \"insertion_strategy\", \"obfuscation_strategy\"])\n", "print(f\"\\nDistinct attack configurations among the {len(injected)} injected files: {len(combos)}\")" ] }, { "cell_type": "markdown", "id": "m72", "metadata": {}, "source": [ "**The corpus is 900 injected files built from 337 distinct attack configurations.** It varies along\n", "five independent axes at once:\n", "\n", "* **12 attack families** - JavaScript injection, XSS, SSRF, DDE template injection, XFA/AcroForm\n", " abuse, shellcode-carrying executables, polyglot files, steganographic payloads, ransomware\n", " simulation, URI redirect phishing, object/action injection, and LLM prompt injection.\n", "* **9 source frameworks** - AtomicRedTeam, AMTSO, WICAR, Glasswall, Metasploit, mindcrypt, OWASP,\n", " RanSim and custom - so no single vendor's idea of what an attack looks like dominates.\n", "* **4 payload families** (EICAR, WICAR, AMTSO, RANSIM) across **550 distinct payload variants**.\n", "* **3 insertion points** - after the first `endobj`, before the trailer, before the final EOF - so\n", " the same payload appears at different depths of the file structure.\n", "* **6 obfuscation strategies** - `js_charcode_array`, `js_unescape`, `js_hex_escapes`, `split_concat`,\n", " `js_plain`, `js_base64_atob` - so a model cannot pass by string-matching one encoding.\n", "\n", "And crucially, **1,045 distinct host documents for 1,100 files**: almost every file has its own\n", "carrier. A model cannot succeed by memorising the container.\n", "\n", "#### The variety is graded, which is what makes it a measuring instrument\n", "\n", "The families are not equally hard, and that is the corpus's most useful property. Counting CIC\n", "suspicious flags per family splits it cleanly in two:\n", "\n", "| | families | flags raised | % raising **zero** flags |\n", "|---|---|---|---|\n", "| **Loud** | `javascript_injection`, `object_action_injection`, `ransomware_simulation`, `shellcode_embedded_exe`, `polyglot_file`, `xfa_acroform_injection` | 1.2 - 2.3 avg | **0%** |\n", "| **Silent** | `dde_template_injection`, `ssrf`, `llm_prompt_injection`, `cross_site_scripting`, `uri_redirect_phishing`, `steganographic_payload` | 0.2 - 0.3 avg | **71 - 81%** |\n", "\n", "Six families always announce themselves through a keyword any scanner would catch; six are silent\n", "71-81% of the time. Overall the malicious files spread across the whole range - 336 raise no flag,\n", "266 raise one, 230 raise two, 68 raise three or more.\n", "\n", "This gives the evaluation a built-in difficulty scale rather than a single pass/fail:\n", "\n", "* The **loud** families verify that a model has basic competence - failing `object_action_injection`,\n", " where `launch` = 1.0 in every file, means the model is not reading PDF structure at all.\n", "* The **silent** families are the real test: a threat that raises no conventional flag. This is where\n", " a model has to reason about what the content *does* rather than match a keyword.\n", "* Because both are present in one corpus, a model's score profile across families is informative in\n", " a way a single averaged number is not - and results should be reported per family for exactly\n", " that reason.\n", "\n", "#### What this corpus can and cannot establish\n", "\n", "* **It can** measure evasion resistance: whether a model finds threats that do not announce\n", " themselves through the CIC keyword vocabulary. 37.3% of these files raise no such flag, so a\n", " keyword-driven scanner is guaranteed to miss a large share of them - and any model that beats that\n", " is demonstrating something real.\n", "* **It can** compare models to each other fairly, since every model faces the identical 1,100 files,\n", " the same 337 attack configurations and the same graded difficulty.\n", "* **It cannot** stand in for a general accuracy claim on real-world PDFs. Graph 7 is explicit: the\n", " host documents cover about a fifth of the map. A model scoring well here has been shown to resist\n", " a wide range of *attacks*, not to handle the full diversity of *documents*.\n", "\n", "That distinction is the honest framing for Part 2: the corpus is a strong instrument for measuring\n", "**security reasoning under evasion**, and a weak one for measuring general-purpose accuracy." ] }, { "cell_type": "markdown", "id": "9dd96d19", "metadata": {}, "source": [ "# Part 2 - Preparing the synthetic corpus for evaluation\n", "\n", "Part 1 ended on a negative result. The 32 structural features of the CIC vocabulary separate real\n", "malware from real benign files well, but they **do not react to our injected payloads at all**: the\n", "correlation with the target is strong on the Real corpus and collapses to about **-0.07** on the\n", "Synthetic one, and when all features are given to UMAP at once the two corpora land in different\n", "regions of the map.\n", "\n", "That was the result Part 1 was designed to produce, but it has a direct consequence here: **the CIC\n", "feature table is the wrong input for Part 2.** If the features cannot see the payload, then no\n", "amount of analysis of those features can tell us whether our corpus is varied enough to test a model\n", "on - and those features certainly cannot be handed to a model as evidence.\n", "\n", "So Part 2 goes back to the **1,100 actual PDF files** and builds its own table directly from them.\n", "\n", "## What this notebook does\n", "\n", "1. **Extract** a table from the raw PDFs, one row per file, and join the manifest that records the\n", " ground truth.\n", "2. **A short EDA** on that table - descriptive statistics, missing values and the checks for useless\n", " columns - covering the ground Part 1 could not cover on the synthetic half.\n", "3. **Check the variety**: three graphs asking whether the corpus contains enough different attack\n", " set-ups to work as a measuring instrument, rather than the same test repeated 900 times.\n", "4. **Embed and map** the extracted text with UMAP, using a representation that can actually see the\n", " payloads.\n", "5. **Elbow analysis** with three clustering algorithms, to find the natural groups in the corpus.\n", "\n", "Evaluating the models is the next stage of the project. Everything here, extraction included, runs\n", "inside this notebook; nothing was computed somewhere else and pasted in.\n", "\n", "## One design decision: text, or numeric features?\n", "\n", "We keep **both**, because the two things we want to do need different inputs:\n", "\n", "* The EDA needs **columns**. There is no `describe()`, no silhouette score and no elbow curve for a\n", " single long block of text.\n", "* The evaluation needs the **text itself**, because the whole point is to give a model the contents\n", " of a PDF and ask whether it is malicious. A feature like `js = 3` is exactly the thin summary that\n", " Part 1 showed is not enough.\n", "\n", "So the extraction produces one row per PDF with three groups of columns: **(A)** the text the model\n", "will read, **(B)** numeric features measured from the file, **(C)** the ground truth and some checks\n", "that the extraction worked. Step 1 measures the corpus first, to decide how group A should be built:\n", "the right shape for that text is not obvious, and a bad choice would delete the very payload we are\n", "trying to test against." ] }, { "cell_type": "markdown", "id": "f5d540e5", "metadata": {}, "source": [ "## Step 0 - Setup\n", "\n", "There are two inputs: the PDF files themselves, and the manifest. The manifest is the ground truth\n", "for the synthetic half - it records which payload was injected into which file, and how." ] }, { "cell_type": "code", "execution_count": 33, "id": "4efd52e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PDFs on disk : 1100 files, 855 MB\n", "manifest : (1100, 11)\n", "\n", "The manifest joins onto the filenames 1:1 -\n", " names matching: 1100 of 1100\n" ] }, { "data": { "text/html": [ "
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indexsource_fileoutput_fileinjection_typeframeworkbase_payloadvariant_usedinsertion_strategyobfuscation_strategyseedtimestamp
0008036c5a50a93da84c5c45ba468c58159d75281e.pdfxfa_acroform_injection_AMTSO_0000.pdfxfa_acroform_injectionAMTSOEICARX5O!p%@ap[4\\pzx54(p^)7cc)7}$eicar-standard-ant...before_final_eofNaN422026-07-19T07:34:39.081102
110a29925ccc5e6299e132a73325956a3abef6dd26.pdfjavascript_injection_WICAR_0001.pdfjavascript_injectionWICAREICAReicar-standard-antivirus-test-file!$h+h* x5o!p...after_first_endobjjs_plain422026-07-19T07:34:39.086525
220e21835a42a6df2405496f62647058ff855743c1.pdfpolyglot_file_Glasswall_0002.pdfpolyglot_fileGlasswallEICAR$H+$H* X5O!p%@ap[4\\pzx54(p^)7cc)7}$Eicar-stand...before_final_eofNaN422026-07-19T07:34:39.353552
\n", "
" ], "text/plain": [ " index source_file output_file injection_type framework base_payload \\\n", "0 0 08036c5a50a93da84c5c45ba468c58159d75281e.pdf xfa_acroform_injection_AMTSO_0000.pdf xfa_acroform_injection AMTSO EICAR \n", "1 1 0a29925ccc5e6299e132a73325956a3abef6dd26.pdf javascript_injection_WICAR_0001.pdf javascript_injection WICAR EICAR \n", "2 2 0e21835a42a6df2405496f62647058ff855743c1.pdf polyglot_file_Glasswall_0002.pdf polyglot_file Glasswall EICAR \n", "\n", " variant_used insertion_strategy obfuscation_strategy seed timestamp \n", "0 X5O!p%@ap[4\\pzx54(p^)7cc)7}$eicar-standard-ant... before_final_eof NaN 42 2026-07-19T07:34:39.081102 \n", "1 eicar-standard-antivirus-test-file!$h+h* x5o!p... after_first_endobj js_plain 42 2026-07-19T07:34:39.086525 \n", "2 $H+$H* X5O!p%@ap[4\\pzx54(p^)7cc)7}$Eicar-stand... before_final_eof NaN 42 2026-07-19T07:34:39.353552 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import math\n", "import random\n", "import re\n", "import time\n", "import zlib\n", "from collections import Counter\n", "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "# Global seed. Every stochastic step in this notebook - the probe samples, UMAP,\n", "# KMeans - is keyed to it, so a re-run reproduces these exact numbers.\n", "SEED = 42\n", "random.seed(SEED)\n", "np.random.seed(SEED)\n", "\n", "# Files drawn per probe in Step 1. 60 is a sample, not the full corpus: each probe\n", "# reads whole PDFs off disk (up to 76 MB each), so a census would cost minutes for\n", "# an answer that is already unambiguous at this size.\n", "PROBE_N = 60\n", "\n", "sns.set_theme(style=\"whitegrid\")\n", "plt.rcParams[\"figure.figsize\"] = (10, 5)\n", "plt.rcParams[\"axes.titlesize\"] = 13\n", "pd.set_option(\"display.max_columns\", 60)\n", "pd.set_option(\"display.width\", 160)\n", "\n", "# The 1,100 generated PDFs. Same files as the Hugging Face repo's Output_PDFs/ folder.\n", "PDF_DIR = Path(\"../ARCHIVE/GENERATION/V4/Output_PDFs\")\n", "\n", "HF_BASE = (\"https://huggingface.co/datasets/Cyber-security-final-project/\"\n", " \"Generated_Injected_PDFs_HARMLESS/resolve/main/Datasets/\")\n", "manifest = pd.read_csv(HF_BASE + \"injection_manifest_combined.csv\")\n", "\n", "pdf_paths = sorted(PDF_DIR.glob(\"*.pdf\"))\n", "total_mb = sum(p.stat().st_size for p in pdf_paths) / 1e6\n", "\n", "print(f\"PDFs on disk : {len(pdf_paths)} files, {total_mb:,.0f} MB\")\n", "print(f\"manifest : {manifest.shape}\")\n", "print()\n", "print(\"The manifest joins onto the filenames 1:1 -\")\n", "print(\" names matching:\", len(set(manifest['output_file']) & {p.name for p in pdf_paths}),\n", " \"of\", len(pdf_paths))\n", "manifest.head(3)" ] }, { "cell_type": "markdown", "id": "bc81fe93", "metadata": {}, "source": [ "## Step 1 - Designing the extraction\n", "\n", "A PDF is not a text file. It is a binary container of numbered objects, and most of what is inside\n", "those objects is compressed. Turning a PDF into something a model can read means making three\n", "choices, and each one can quietly delete the payload:\n", "\n", "1. **How much of the file do we keep?** The files here run from 306 bytes to 76 MB, so something has\n", " to be cut.\n", "2. **What do we do with the compressed parts (the \"streams\")?** Keeping them as they are fills the\n", " text with binary noise; throwing them away is the obvious alternative.\n", "3. **Do we decompress them?** Decompressing every stream is slower and sometimes fails.\n", "\n", "We do not guess at any of these. Each one is decided by measuring the corpus first, because a wrong\n", "choice produces a dataset that *looks* perfectly fine and quietly makes the whole evaluation\n", "meaningless - the model would be marked wrong for missing a payload we deleted ourselves.\n", "\n", "### Probe 1 - where in the file is the payload?\n", "\n", "The manifest records an `insertion_strategy` for each file: `after_first_endobj`, `before_trailer`\n", "or `before_final_eof`. If the payloads all sit near the start, then simply cutting off the end of\n", "the file is safe. Rather than assume that, we check.\n", "\n", "The probes below run on a **random sample of 60 injected files, not the whole corpus**. Each probe\n", "reads every sampled PDF from start to finish and some of them are tens of megabytes, so checking all\n", "1,100 would take minutes to answer a question that is already clear at n = 60 - the results are\n", "one-sided rather than borderline. The sample uses the notebook-wide `SEED = 42`, so the numbers\n", "quoted in the findings can be reproduced by re-running the cell." ] }, { "cell_type": "code", "execution_count": 34, "id": "795fd158", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Payload position as a fraction of file length, by insertion strategy:\n", " count min 50% max\n", "insertion_strategy \n", "after_first_endobj 22.0 0.000 0.003 0.161\n", "before_final_eof 22.0 0.982 0.998 1.000\n", "before_trailer 16.0 0.972 0.998 1.000\n", "\n", "Payloads sitting past the halfway point: 38 of 60\n", "Payloads not found at all : 0\n" ] } ], "source": [ "# Byte offset of the first ground-truth marker, as a fraction of file length.\n", "PROBE_MARKERS = re.compile(\n", " rb\"EICAR|AMTSO|WICAR|RANSIM|IGNORE PREVIOUS|POLYGLOT|LSB-STEGO|phishing\\.|\"\n", " rb\"169\\.254|/JavaScript|/Launch|