{ "cells": [ { "cell_type": "markdown", "id": "2ac4c7e9", "metadata": {}, "source": [ "**Author:** Dr. Mallarapu \n", "**Created:** 2026-07-27 \n", "**Course:** SEAS 8414 \u2014 Security Analytics\n", "\n", "---\n", "\n", "### Goal of this notebook\n", "\n", "Train and audit detectors on the WUSTL-IIoT industrial testbed, where a false alarm can stop a process.\n", "\n", "### What you will learn\n", "\n", "1. Read a majority-class baseline before trusting any accuracy figure.\n", "2. Find the strongest single feature, then test it by dropping it and refitting.\n", "3. Tell duplicate inflation apart from genuine signal.\n", "4. Report per-group recall, because the rare classes carry the risk.\n", "\n", "### Where this connects to the course text\n", "\n", "The text builds a defence pipeline; this notebook trains a classifier and audits it. The links below are to specific chapter objectives that share an *analytic move*, not to matching subject matter.\n", "\n", "- **Chapter 6: Digital Twins for Remediation Simulation** \u2014 Learning objective 2 (section 6.1) treats fidelity as a **promotion gate**. An in-distribution score is not a deployment estimate, which is the gate this notebook refuses to pass.\n", "- **Chapter 11: Formal Protocol Verification** \u2014 Section **11.1.2**, titled *Proved, tested, and hoped*, asks you to separate exactly those three. (Chapter 11 lists its objectives in \u00a711.0, not \u00a711.1 as the other chapters do.) The ablation does that job here: it tests whether the headline survives.\n", "- **Chapter 12: Autonomous Remediation and Safety Verification** \u2014 Learning objective 1 (section 12.1) assembles evidence into a **safety case** with stated assumptions. Section 13 is that safety case for a model score.\n", "\n", "---" ] }, { "cell_type": "markdown", "id": "b7aa97e4", "metadata": {}, "source": [ "# Industrial-IoT Intrusion Detection on WUSTL-IIoT-2021\n", "### Model comparison + per-attack-family recall + validity audit (1.19M SCADA-testbed flows)\n", "\n", "**Abstract:** WUSTL-IIoT-2021 (Zolanvari et al., 2021) is network telemetry from a real **Industrial IoT** testbed \u2014 a water-storage-tank SCADA process at Washington University. It carries 1.19M flows labelled benign vs attack (DoS, Reconnaissance, command-injection, Backdoor). Unlike the consumer-IoT and enterprise-network datasets elsewhere in this series, this is an operational-technology (OT) setting. We compare four learners and report recall per attack family \u2014 where command-injection (CommInj) and Backdoor are vanishingly rare." ] }, { "cell_type": "markdown", "id": "998a002a", "metadata": {}, "source": [ "## 1. Research problem\n", "\n", "**Task:** Flag an IIoT network flow as benign or attack in an OT environment. In that setting, a false positive can trip a physical process and a missed intrusion can damage equipment. The set is 93% benign and the malicious 7% is ~90% DoS. So the honest challenge is the **rare** families (command-injection and Backdoor (a few hundred flows each)), not the DoS flood." ] }, { "cell_type": "markdown", "id": "cce9ca0d", "metadata": {}, "source": [ "## 2. Literature review\n", "\n", "- **Zolanvari, Teixeira, Gupta, Khan & Jain (2021)** \u2014 the WUSTL-IIoT-2021 testbed and dataset for IIoT cyber-security research.\n", "- **Zolanvari et al. (2019)** \u2014 an *earlier, separate* study (not this 2021 dataset): *Machine Learning-Based Network Vulnerability Analysis of IIoT* (IEEE IoT Journal): the ML-IDS motivation.\n", "- **Morris & Gao (2014)** \u2014 industrial control-system attack datasets.\n", "- **Sommer & Paxson (2010)** \u2014 the closed-world ML critique.\n", "\n", "**Related approaches and their known caveats** \u2014 drawn from the wider literature; these are **not** measurements reproduced on this exact corpus:\n", "\n", "| Reported approach | Known caveat |\n", "|---|---|\n", "| Zolanvari et al. \u2014 ML-IDS on IIoT flows (2019 study; 2021 dataset release) | different corpus/protocol from this run; extreme imbalance, DoS dominates the attack class |\n", "| Flow-based OT intrusion detectors | rare families (CommInj/backdoor) are the hard, safety-critical cases |\n" ] }, { "cell_type": "markdown", "id": "15910b66", "metadata": {}, "source": [ "## 3. Dataset provenance & honesty caveats\n", "\n", "| Property | Value |\n", "|---|---|\n", "| Source | Kaggle `annaamalaiu/wustl-iiot-2021-dataset` (Zolanvari et al., 2021) |\n", "| Rows | 1,194,464 flows. **The released CSV spans ~7 h** (2019-08-19, by its own StartTime/LastTime columns); the ~53 h figure quoted for WUSTL-IIoT-2021 refers to the raw collection campaign, not this flow product. |\n", "| Label | `Target` 0/1 (~7% attack); family `Traffic` (DoS/Reconn/CommInj/Backdoor) |\n", "| Access | Kaggle API token required |\n", "\n", "**Honestly:** the attack class is ~90% DoS, so aggregate accuracy is base-rate inflated. The rare command-injection (CommInj) and Backdoor families are the real test. Worst-first per-family recall makes them visible. IP/port/time identifiers are dropped so the model uses flow behaviour, not endpoint identity.\n", "\n", "### Before you run this: getting the data\n", "\n", "This notebook downloads its own data on the first run, then caches it. You do not fetch anything by hand.\n", "\n", "**Dataset:** Kaggle `annaamalaiu/wustl-iiot-2021-dataset` -> `/tmp/kg_wustl`. It is about **391 MB** on disk.\n", "\n", "**One-time setup.** Sign in at kaggle.com, open **Settings**, and under **API** choose **Create New Token**. Kaggle hands you a `kaggle.json` file. This notebook does *not* read that file. It reads a plain key file, so convert it once:\n", "\n", "```bash\n", "mkdir -p ~/.kaggle\n", "python3 -c \"import json;print(json.load(open('kaggle.json'))['key'],end='')\" > ~/.kaggle/access_token\n", "chmod 600 ~/.kaggle/access_token\n", "```\n", "\n", "Never paste the token into a cell, a commit, or a screenshot. If it leaks, revoke it from the same Settings page.\n", "\n", "**If the loader fails:**\n", "\n", "- `FileNotFoundError: ~/.kaggle/access_token` - you created `kaggle.json` but not the key file. Run the command above.\n", "- `401 Unauthorized` - the key is wrong, or a trailing newline crept in.\n", "- `403 Forbidden` - open the dataset page on Kaggle while signed in, accept its terms, then re-run the cell.\n", "\n", "The cache sits under `/tmp`, which macOS clears on reboot. To keep it, move the folder somewhere durable and symlink it back. Do **not** edit the path in the code cell below: that changes a code cell and invalidates the stored outputs you are reviewing." ] }, { "cell_type": "markdown", "id": "07682443", "metadata": {}, "source": [ "## 4. Solution design\n", "\n", "The methodology is deliberately two-track. We *earn* a headline score with standard modelling, then *interrogate* it with a validity audit. Only a verdict that survives both is reported. The diagram below is the shape of every notebook in this series." ] }, { "cell_type": "markdown", "id": "7cca0baf", "metadata": {}, "source": [ "**Figure 4.1 \u2014 Solution design (methodology).**\n", "\n", "\"Figure" ] }, { "cell_type": "markdown", "id": "9930c75f", "metadata": {}, "source": [ "## 5. Implementation architecture\n", "\n", "Five stages \u2014 ingestion, preprocessing, modelling, evaluation, and a parallel validity-audit path \u2014 feed a single graded results ledger. Leakage defences (dropping label-derived and identifier columns) live in preprocessing, before any model sees the data." ] }, { "cell_type": "markdown", "id": "98ea8569", "metadata": {}, "source": [ "**Figure 5.1 \u2014 Implementation architecture.**\n", "\n", "\"Figure" ] }, { "cell_type": "markdown", "id": "427465da", "metadata": {}, "source": [ "## 6. Data acquisition & preparation\n", "\n", "Every line below is commented so a student can re-run and modify each step. The cell ends by producing the standard analysis variables: `df`, `X` (clean numeric features), `y` (binary label), `feat` (feature names), and `family`. `family` is the per-group label used for the recall breakdown. It is an attack family on the intrusion corpora, but a transaction type, merchant category or malware category on the fraud/malware ones." ] }, { "cell_type": "code", "execution_count": 1, "id": "2d8f4e64", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import time, warnings; warnings.filterwarnings('ignore') # keep output clean\n", "import numpy as np, pandas as pd # numerics + dataframes\n", "import matplotlib.pyplot as plt # static plots (embed in HTML+PDF)\n", "plt.rcParams['figure.dpi'] = 120 # crisp figures\n", "RANDOM_STATE = 0 # single seed used everywhere\n", "np.random.seed(RANDOM_STATE) # reproducible sampling\n", "NEG_WORD, POS_WORD = 'benign', 'attack' # class names (overridden by some loaders)" ] }, { "cell_type": "code", "execution_count": 2, "id": "d9054f59", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loaded 1,194,464 IIoT flows x 39 features; attack rate 0.0728; families ['Backdoor', 'CommInj', 'DoS', 'Reconn', 'normal']\n" ] } ], "source": [ "import os, glob\n", "# WUSTL-IIoT-2021 (Zolanvari et al., 2021): network telemetry from a Washington University INDUSTRIAL\n", "# IoT testbed (a real water-storage-tank SCADA process), 1.19M flows labelled Target 0/1 with the\n", "# attack family in `Traffic` (DoS / Reconnaissance / command-injection / Backdoor). Self-contained.\n", "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n", "DEST = '/tmp/kg_wustl'; os.makedirs(DEST, exist_ok=True)\n", "if not glob.glob(DEST + '/**/*.csv', recursive=True):\n", " import kaggle; kaggle.api.authenticate()\n", " print('downloading WUSTL-IIoT-2021 (one-time)...')\n", " kaggle.api.dataset_download_files('annaamalaiu/wustl-iiot-2021-dataset', path=DEST, unzip=True, quiet=True)\n", "f = sorted(glob.glob(DEST + '/**/*.csv', recursive=True), key=os.path.getsize, reverse=True)[0]\n", "df = pd.read_csv(f, low_memory=False); df.columns = [str(c).strip() for c in df.columns]\n", "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'\n", "df['y'] = df['Target'].astype(int)\n", "df['family'] = df['Traffic'].astype(str).str.strip() # normal / DoS / Reconn / CommInj / Backdoor\n", "# Drop labels and flow IDENTIFIERS (IP/port/time/ip-id) so only behaviour features remain.\n", "DROP = ['Target', 'Traffic', 'y', 'family', 'StartTime', 'LastTime', 'SrcAddr', 'DstAddr',\n", " 'Sport', 'Dport', 'sIpId', 'dIpId']\n", "feat = [c for c in df.columns if c not in DROP]\n", "from sklearn.preprocessing import LabelEncoder\n", "X = df[feat].copy()\n", "idlike = [c for c in X.select_dtypes(include='object').columns if X[c].nunique() > 0.5*len(X)]\n", "X = X.drop(columns=idlike) # drop id/timestamp-like leaky columns\n", "for c in X.select_dtypes(include='object').columns:\n", " X[c] = LabelEncoder().fit_transform(X[c].astype(str))\n", "X = X.apply(pd.to_numeric, errors='coerce').replace([np.inf,-np.inf],np.nan).fillna(0.0)\n", "X = X.clip(-1e15, 1e15); X = X.loc[:, X.nunique() > 1] # float32-safe; drop constants\n", "import re\n", "_seen, _cols = {}, []\n", "for _c in X.columns: # unique LightGBM-safe names\n", " _c = re.sub(r'[^0-9A-Za-z_]+', '_', str(_c)).strip('_') or 'f'\n", " _seen[_c] = _seen.get(_c, -1) + 1\n", " _cols.append(_c if _seen[_c] == 0 else f'{_c}_{_seen[_c]}')\n", "X.columns = _cols; feat = list(X.columns)\n", "y = df['y'].to_numpy(); family = df['family'].to_numpy()\n", "print(f'loaded {len(df):,} IIoT flows x {len(feat)} features; attack rate {y.mean():.4f}; families {sorted(set(family))}')" ] }, { "cell_type": "markdown", "id": "f473e712", "metadata": {}, "source": [ "## 7. Exploratory data analysis" ] }, { "cell_type": "code", "execution_count": 3, "id": "9bc54223", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# --- EDA 1: class balance and the attack-family mix ---\n", "fig, ax = plt.subplots(1, 2, figsize=(11, 4))\n", "df['y'].map({0:NEG_WORD,1:POS_WORD}).value_counts().plot.bar( # counts per class\n", " ax=ax[0], color=['#2a9d8f','#e76f51']); ax[0].set_yscale('log')\n", "ax[0].set_title(f'Class balance ({NEG_WORD} vs {POS_WORD})'); ax[0].set_ylabel('records (log)')\n", "df.loc[df.y==1,'family'].value_counts().head(8).plot.barh( # top attack families\n", " ax=ax[1], color='#e76f51'); ax[1].invert_yaxis(); ax[1].set_title('Top attack families')\n", "plt.tight_layout(); plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "id": "225cfc33", "metadata": {}, "outputs": [ { "data": { "image/png": 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h8fYxa9Ysdyyv4xL/xCz7JBgqyhg2bJg7gaXP8yoK8mffsk/qnwqMVJSnz2KtWrWq8TGb2y/8nal7+qwX+3lPbU31utFCnNdff70rZmK/1D1C4gbWkzhW69atQ1UFq0piBbCqHtaqn6qC1fTg6lP/gqTqzd/+9rc2YsQIVwGrFg4KdXRGKgzuv/9+129TIaf6IfmVkmFoNSGqkNQbswJOBXX+mVO/6jRo+gN94YUXut6DatWhg2CFYWGi0FAVkjqh4lNVrN82IUjRaNSd5NHq8Kos1fQd9eZSe4wwBcU6QaGTUKq6UBCrA231qnr++eeDHprbj3oP1FROPQ/1PqhwM0zUn1PT4rSfFbbrNaz9rrP0YaGx/O53v3MH6wqLFWSrOjv2dQMAPp1wrWlWk/5G+PcjeP5+YF/VPxUoDB061J3E1t9/zapknwRH7Q91ERW2qG2a2jZ++eWXvE4CcNNNN7lcRe0mtvX9i78z9bf+y3HHHWcvv/yyO7nCfql7hMQNRE3rC6pad0sW+akvCl7HjBlT2U/Jpx6X/vSZoCngVC9iLaQxaNAgN2Vh7NixLkwMA+1PVRJPnjzZBV6qHldbhzDtZ/0BVd8sHWBee+217o3Zr/4IWpgXNIutJNZrRNPp/UXDvvnmm8o2MkEv/KPKdU2B8/vDqpetDpjCFBJrwRsttvKf//zH9ajSCTNVEoeh4l4nyxSuq12MLj6FsuqJGaYQNkyhcHWPPvqoO6mnSid/Grnev0eNGhX00ACEkN4jNIW+On8aathO1jVW/jTtmha01m06xqSKuO6p761Osq5bt85NiY99PbBPwkHHOCp00fo07JP6pXUw/vWvf7mirNjWRAp+VVSmz5UqetvcfuHvTP3RZ2hV3qvVIPul7hESh5wfECrw0oIcsVTpp7MoYaHp1AqJq1OQE5aQWEGcWjr4C5n4/EA2aAq+VB2p0EtTwjQmVfopMFEoG7QJEya4s90K2PW9QmL1blKFXxgWGwnzgmY+HaQrzNb20gGFqu5VBas+S2HoU6fZALHV/wqJtY/DRgcHev6FjQ4kq9NZbp0MCBOdOFGPdn/hxNiWLWGgtkpaOTp29WgAqI1OxE2cONHNuov9G6aqPP9+BE+zazSVe8qUKRvd99VXX7GftgMFXapQVfioXquxLffYJ+HhT5lXoK/2abxO6o9OMGo2pdqu1dR6beedd7bLL7/ctS/UbFq9f2kNHJ/CShWCxN6G7UufpVWkpjVpevfuzX6pY4TEIac+kfoAr4ovBYU+BccKEv1pKkHyF2jSmbbYxZr8wKR6X8kg6QytQuKaqqDDEBKrD/EDDzzg+u34IacC2bPPPtvC4I9//KMLl9S71h+fAveaDvaDEOYFzWIpEPZ7JoeJqne0am/s9tP0xKysLAuTnJwcV5H9/fffu5VuY6ldSxAOO+wwt800BU0nJmKpXcd+++1nYaG2MTohoOpwnSDTga9O8qmKJUzUT1zvLdWD7DC+dgAES+9fah2marCrr77a3ab3Y80c0zGLTiwiHLQIkWZ3qY2Qv1907KYQ88orrwx6eDsUnaTW7DUVnGhx8dpmTrJP6vfYRp/hY+kztFo0anamH+KzT+qPQsZXXnmlxhYUmpWqdpBqs6hWLVqgWsfMaofWtGlT9zjNStbnkeHDh9fjqBsHFUVW7xGtz3/KI1R4pUUf2S91L+LV1McADeIPiqjaNOigWNO9RaGDVp6vHmRr0b3qVdD1TYuricIb9YONfdoryFZFog5Wg6ZpdgpEtO38ftM6wNN21O1hCDf1Zh07Ph3YqDJEz9GgqYWDwkM1uPfHp2rJP/3pTxstyBYUTfXTitI1VXHqrGiQNJVfbRtuu+0294dXVVkKFPWhQgdKYaHqa53JV8/a6v2//IVY6ps+8GpfXnzxxa6/b/X3QfUY1wq9YaATdx9//LE74M3MzHTPyWnTprmK+9pWdK5vmjmhvy1q36EZFlowU1/VY1yzGACgOlVx6YO+gkb1LNT7sqpTFUDqhDu2P80c1N8UTdl++OGH3ew4rT4val2lD/M63tZt+vujk5QKV+666y73t0nrSdBuou7o77oCLlUS11Tl6LcWY5/UHx27asaD3pP0+UnFGPoMrc/0WnNDBTnsk3DQMeeaNWvcMbJPLQL1OVNh/gUXXOAKQbTf/Fm2qFv6/KTPetrm+jylVqE6Gaz1m/TZfrfddmO/bA8KiRF+/fr183JycqrcNmXKFK9Dhw5eWDz99NNeWHXp0sVd4uLiKr/XZeedd/b2228/7+WXX/bCYNCgQd4rr7zivm/evLn7+uqrr3pDhgzxwmDo0KHegw8+WGV8Dz/8sHf88cd7YfDRRx95rVq18i688EIvNTXVu/zyy7327dt7kyZN8sJi+PDh3pFHHulNmDDBa9q0qft62GGHeffcc0/QQ/MqKiq8v/3tb17Pnj29Jk2aeLvuuqt35513euXl5V6YZGRkeGVlZV4YzZw50wu7zMxMLxqNuu/btm3r5efnu+t6PoaFnntvv/125XjlxRdfdK9tAKhJUVGRd/XVV7v3teTkZG+fffbx3nnnHTZWPercubPOfNd4mT9/fuXjpk2b5h1++OHuWEPv8aeddpq3YsUK9lUdGzx4cK37o3oMwD6pH88995x36KGHem3atPESEhLc5yldf+211zZ6LPsk+NdPr169Nrr9k08+8fbff38vJSXFfe685JJLvPXr1wcyxh3d/fff7w0YMMBr0aKFe720a9fOO/300705c+Zs9Fj2S92hkrgBnZl/6qmn7MMPP3S91rTogKYPPf7446Fa7V1VpWr+Xr1CMiwVHOeff36Vth1howWvtD817eWjjz6y3/72t66FwptvvuluC5r6qqoVhvqbqepVZ1HVw0njC8tUTp3R1Rl5jVVn6DWlPgwLmsVWY8+fP99NUfKrOHWWWouxTZ8+PfBtV1N7mNpuD4qmc6lSTGeVw0ZtWFQlolkVOvsdpkUnfXquqVr8oIMOclVeqrrX81F/XzSFKwz0d06VNqJ2J5qpoJXY/RkCAAAAAIC6RUjcgKjXmqbgahqKpme98MIL7kN+WGj6svr6KhTx+8co/NKUP00NwJZR/9w33nijMuQ8+uijqyzCEjSF/5q+6Y9Pvf4U3oTBX/7yF7vuuus2ul2LX11zzTUWBm3atHELwanZvsLryZMnu+mXut0PxcIQzMUKWzCn9xW992lhj+pteDQFKUhqX6MFMnXROHUyTycq9tlnHwsLv63JLrvs4qYE+z3Xbr75ZtfeIQz69u1rL730klu89cADD3StJ/Q81HRlnbQAAAAAANQtQuIG5vbbb3e9pd555x3r37+/hYlWjtYH+HPPPddV02lxKY1VX2+55RYLA4ULGqP6wVYPvdT7N2haQG/MmDEb3X7ppZe6avKgDR061FUNV6fqYjWQD1pDCDm1EIUuCg7VK27SpEkuMFZV8VtvvRXo2FRNqrCw+oq/em3rxE9YHHfcca4aWwt6Vu9JrL7KYaHKcIXF//nPf1wPZS2aqbAzTJXtYaX3Gb2edTJA79darE59K7Wwp4J3AAAAAEDdIiQOMTXkrj5VWVWcmt6vyk19r/tLS0stDPSBPjc3t8qiZhpru3btbOXKlRYGat+gafNayEwVsKqIvfXWW12l2oUXXhj08EIfcoZ1fArh5LzzznMtWKovTKjVzdUGJWx0YkIr5Cr8Ugjmr5Ib1HuNxlO9KlyvYVVnqz1BWGg7aaGPtLQ0Cytty3fffdc9NxX+6z2mU6dObgbIJZdc4qp2g6RVtBVgq5K4ffv2LsDWcxAAAAAA0DglBD0A1G7u3LkNavMoDF6+fLkLHHbeeWcXkKjlhEKmsFAo/Nprr1WunLzrrru61Zf32GOPQEPiO+64o7Kns/99bMgZdD9Yrd4qJSUlld/71DqhZ8+eFiS/z7ROmMS2G1DwqXYE6ucdRgrczzrrrFC81yhYP+KII9zrNnb7qYdykyZNLEzUukErcQf9vKuJKsMVDKtVgtphqDexZlToRIrceOON7n0nyJBYJ8Z0cuKqq66yzp07u9fwnXfe6b6OHDnSwtQWY9y4cZVBtnpRq0UGAAAAAKDuERKHmD68y4QJE9y05T333NMOOeQQCytVG/7www/uw7yChuOPP96FnuoHGxaamq4xKSRWgP3zzz+78CboqfR+lauqD2MrXv2QU72og6TewzV9r/Gp7cmwYcMsSBMnTnRfVe2q/qphpmBYPcX/+9//ugUA9ZzU9Pl7773X9SYO8r3m6aefrvw+zNQ3d8iQIS40rN6T+IYbbrAg6STK6aef7k5I6WRZdW3btnWBbJBUba/FT2MXm9SCmQcccEBoQmK1r9F2VE92PSf1t0Un0FQBrXYjAAAAAIC6RbuJBtCDWIFCr169XFCsD8mXXXaZNQSq6lTlaVBT6Guibadp3yeddJLbtqoiTkpKcuHI2LFjgx6eC0DCPOVbixMOHjzYwkrV4AqWwrSgY3Xav4WFhe617FdxKtjWiYugn4Oq0lTwr9YD6pm8++67Wxipr29t1FoEm5/1MXv27CoLYq5bt85VZquNRxj07t3bHnrooSrvN6rSvvjii93fQgAAAABA3SIkDjktcKTptppePXnyZBeOzJw50xoKLbCnkFvhYhgpdNC09RkzZrjQOGia7q+Q83e/+52lp6db2Gj6vManALFr164WNlogUX1Wi4qK7JRTTnFT/dVKJEzatGnjWojELrhWUFDgqk5XrVplQfv888/t+eefd+87qnrVvlZoHHTLk4bgxx9/tFmzZtlee+3lAneFnJoJorBd7SXC0rZDPZH1d0SvF1UTL1q0yLWgUBuMmhbODIJmeKiXvfpl+zQLRJXjWgwVAAAAAFC34ur496GOaTEwBcSy3377hSJEqk6VZwoOVfmlFhMKHLQavdoQqLLzmGOOCXqIrt/qY489Zpdeeqndd999rspZ2/aVV16xiy66yE1lDgOFceoVqpYdqnZ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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# --- EDA 2: feature correlation + a 2-D PCA projection ---\n", "from sklearn.preprocessing import StandardScaler # scale before PCA\n", "from sklearn.decomposition import PCA\n", "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", "topv = X[feat].var().sort_values().tail(12).index # 12 highest-variance features\n", "im = ax[0].imshow(X[topv].corr(), cmap='coolwarm', vmin=-1, vmax=1) # correlation heatmap\n", "ax[0].set_xticks(range(len(topv))); ax[0].set_xticklabels(topv, rotation=90, fontsize=7)\n", "ax[0].set_yticks(range(len(topv))); ax[0].set_yticklabels(topv, fontsize=7)\n", "ax[0].set_title('Feature correlation (top-variance)'); fig.colorbar(im, ax=ax[0], shrink=0.7)\n", "samp = X.sample(min(5000, len(X)), random_state=RANDOM_STATE) # subsample for a fast PCA\n", "pc = PCA(n_components=2).fit_transform(StandardScaler().fit_transform(samp))\n", "ys = y[samp.index] # aligned labels for coloring\n", "for lab,c in [(0,'#2a9d8f'),(1,'#e76f51')]:\n", " ax[1].scatter(pc[ys==lab,0], pc[ys==lab,1], s=4, alpha=0.4, color=c,\n", " label={0:NEG_WORD,1:POS_WORD}[lab])\n", "ax[1].set_title('PCA projection (2 components)'); ax[1].legend(); ax[1].set_xlabel('PC1'); ax[1].set_ylabel('PC2')\n", "plt.tight_layout(); plt.show()" ] }, { "cell_type": "markdown", "id": "b781400c", "metadata": {}, "source": [ "## 8. Model comparison\n", "\n", "Four diverse learners share one held-out split, ranked by ROC-AUC.\n", "\n", "**Two honesty guards print with the table:**\n", "\n", "1. The models train on a *stratified subsample* of at most 120,000 rows. The full row count is printed above. So every score here is a subsample number, not a full-corpus claim.\n", "2. The **majority-class baseline accuracy** appears *inside* the ranking table. On imbalanced data, 0.99 accuracy can be worse than always guessing the majority class. Judge each model against that baseline, not against 0.5." ] }, { "cell_type": "code", "execution_count": 5, "id": "c37d1047", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "materialized 1,194,464 rows | trained on 120,000 (stratified subsample) | held-out 298,616\n", "MAJORITY-CLASS BASELINE accuracy = 0.9272 (any model must beat THIS, not 0.5, to be interesting)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "best model: XGBoost\n" ] }, { "data": { "text/html": [ "
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modelaccuracyroc_auctrain_s
0XGBoost0.9999631.0000000.3
1LightGBM0.9999131.0000001.1
2RandomForest0.9999400.9999770.6
3LogisticRegression0.9946490.9986800.3
4MajorityBaseline0.9272000.5000000.0
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" ], "text/plain": [ " model accuracy roc_auc train_s\n", "0 XGBoost 0.999963 1.000000 0.3\n", "1 LightGBM 0.999913 1.000000 1.1\n", "2 RandomForest 0.999940 0.999977 0.6\n", "3 LogisticRegression 0.994649 0.998680 0.3\n", "4 MajorityBaseline 0.927200 0.500000 0.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# --- Model comparison: four learners on the same held-out split ---\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import accuracy_score, roc_auc_score\n", "import xgboost as xgb, lightgbm as lgb\n", "\n", "# Stratified split keeps the class ratio in both halves.\n", "Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.25, random_state=RANDOM_STATE, stratify=y)\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "N_MATERIALIZED = len(y) # the full corpus we loaded (see printed count)\n", "# HONEST DISCLOSURE: we do NOT train on all N. We fit on a STRATIFIED subsample (<=120k) because\n", "# these learners saturate long before then on this data. Every headline below is a SUBSAMPLE\n", "# number, not a full-corpus number \u2014 saying otherwise would be the fabrication this course forbids.\n", "if len(Xtr) > 120_000:\n", " Xtr, _, ytr, _ = train_test_split(Xtr, ytr, train_size=120_000, random_state=RANDOM_STATE,\n", " stratify=ytr) # genuinely stratified, not random\n", "MAJORITY_BASELINE = max(np.mean(yte), 1 - np.mean(yte)) # accuracy of 'always predict majority'\n", "print(f'materialized {N_MATERIALIZED:,} rows | trained on {len(Xtr):,} (stratified subsample) | '\n", " f'held-out {len(yte):,}')\n", "print(f'MAJORITY-CLASS BASELINE accuracy = {MAJORITY_BASELINE:.4f} '\n", " f'(any model must beat THIS, not 0.5, to be interesting)')\n", "\n", "models = { # four standard, diverse learners\n", " 'LogisticRegression': make_pipeline(StandardScaler(), LogisticRegression(max_iter=300)), # scaled!\n", " 'RandomForest': RandomForestClassifier(n_estimators=60, n_jobs=-1, random_state=RANDOM_STATE),\n", " 'XGBoost': xgb.XGBClassifier(n_estimators=80, max_depth=6, tree_method='hist', n_jobs=-1,\n", " eval_metric='logloss', random_state=RANDOM_STATE),\n", " 'LightGBM': lgb.LGBMClassifier(n_estimators=80, n_jobs=-1, verbose=-1, random_state=RANDOM_STATE),\n", "}\n", "rows, fitted = [], {}\n", "for name, m in models.items(): # fit + score each model\n", " t = time.perf_counter(); m.fit(Xtr, ytr); fitted[name] = m\n", " p = m.predict_proba(Xte)[:, 1] # positive-class probability on held-out\n", " rows.append({'model': name, 'accuracy': round(accuracy_score(yte, (p>0.5).astype(int)), 6),\n", " 'roc_auc': round(roc_auc_score(yte, p), 6), # 6 dp: a 1.000000 is a red flag, not a win\n", " 'train_s': round(time.perf_counter()-t, 1)})\n", "rows.append({'model': 'MajorityBaseline', 'accuracy': round(MAJORITY_BASELINE, 4),\n", " 'roc_auc': 0.5, 'train_s': 0.0}) # show the baseline IN the ranking table\n", "comparison = pd.DataFrame(rows).sort_values('roc_auc', ascending=False).reset_index(drop=True)\n", "_ranked = comparison[comparison.model != 'MajorityBaseline']\n", "best_name = _ranked.iloc[0]['model']; best = fitted[best_name] # winner by ROC-AUC (excl. baseline)\n", "print('best model:', best_name); comparison" ] }, { "cell_type": "markdown", "id": "fffdeb41", "metadata": {}, "source": [ "## 9. Results\n", "\n", "Diagnostics for the winning model, including **per-group recall**.\n", "\n", "The grouping comes from whatever the loader put in `family`. It is *not* always an attack taxonomy. On the intrusion corpora it is the attack family. On the fraud and malware corpora it is a transaction type, a merchant category or a malware category. On binary corpora it collapses to the positive class.\n", "\n", "Read it accordingly. Where the groups are genuinely rare classes, they reveal whether detection is real. The dominant flood classes do not." ] }, { "cell_type": "code", "execution_count": 6, "id": "49357a08", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "operational FALSE-POSITIVE RATE @0.5 = 0.0000 (5 benign flagged of 276,862)\n", "worst per-family recalls: {'Backdoor': 0.96, 'CommInj': 0.971, 'DoS': 1.0, 'Reconn': 1.0}\n" ] } ], "source": [ "# --- Results for the best model: confusion, ROC, PR, importances, per-family recall ---\n", "from sklearn.metrics import confusion_matrix, roc_curve, precision_recall_curve, recall_score\n", "pb = best.predict_proba(Xte)[:, 1]; pred = (pb > 0.5).astype(int)\n", "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n", "# (1) confusion matrix\n", "cm = confusion_matrix(yte, pred); ax[0].imshow(cm, cmap='Blues')\n", "ax[0].set_title(f'{best_name}: confusion'); ax[0].set_xticks([0,1]); ax[0].set_yticks([0,1])\n", "ax[0].set_xticklabels([NEG_WORD,POS_WORD]); ax[0].set_yticklabels([NEG_WORD,POS_WORD])\n", "for (i,j),v in np.ndenumerate(cm): ax[0].text(j,i,f'{v:,}',ha='center',va='center')\n", "# (2) ROC and PR curves\n", "fpr,tpr,_ = roc_curve(yte, pb); prec,rec,_ = precision_recall_curve(yte, pb)\n", "ax[1].plot(fpr,tpr,color='#264653'); ax[1].plot([0,1],[0,1],'--',c='grey')\n", "ax[1].set_title(f'ROC (AUC={roc_auc_score(yte,pb):.4f})'); ax[1].set_xlabel('FPR'); ax[1].set_ylabel('TPR')\n", "ax[2].plot(rec,prec,color='#e76f51'); ax[2].set_title('Precision-Recall'); ax[2].set_xlabel('recall'); ax[2].set_ylabel('precision')\n", "plt.tight_layout(); plt.show()\n", "\n", "# (3) feature importances + (4) per-attack-family recall\n", "fig, ax = plt.subplots(1, 2, figsize=(13, 5))\n", "imp, names = None, feat # importances, robust to the scaled-LR pipeline\n", "if hasattr(best, 'feature_importances_'): # tree models\n", " imp = best.feature_importances_; names = list(getattr(best, 'feature_names_in_', feat))[:len(imp)]\n", "elif hasattr(best, 'named_steps') and 'logisticregression' in getattr(best, 'named_steps', {}):\n", " imp = np.abs(best.named_steps['logisticregression'].coef_[0]); names = feat # LR pipeline\n", "elif hasattr(best, 'coef_'):\n", " imp = np.abs(best.coef_[0]); names = feat\n", "if imp is not None:\n", " pd.Series(imp, index=names[:len(imp)]).sort_values().tail(12).plot.barh(ax=ax[0], color='#264653')\n", "ax[0].set_title(f'{best_name}: top importances / |coef|')\n", "# Per-family recall, WORST-first so rare, hard classes are visible, not just the dominant floods.\n", "fam_te = df.loc[Xte.index, 'family']\n", "fr = {}\n", "for fam, cnt in fam_te[yte==1].value_counts().items():\n", " if cnt < 5: continue # need a few positives for a meaningful recall\n", " mask = (fam_te==fam).to_numpy(); fr[fam] = recall_score(yte[mask], pred[mask], zero_division=0)\n", "srt = pd.Series(fr).sort_values()\n", "show = pd.concat([srt.head(9), srt.tail(3)]) if len(srt) > 12 else srt # worst 9 + best 3\n", "show = show[~show.index.duplicated()]\n", "show.plot.barh(ax=ax[1], color=['#e76f51' if v < 0.5 else '#2a9d8f' for v in show]); ax[1].set_xlim(0,1)\n", "ax[1].set_title('Per-family recall (worst first; red < 0.5)')\n", "plt.tight_layout(); plt.show()\n", "# Operational numbers, not just figures: false-positive rate and the worst per-family recalls.\n", "tn, fp = int(cm[0,0]), int(cm[0,1])\n", "fpr_op = fp/(fp+tn) if (fp+tn) > 0 else float('nan') # benign wrongly flagged @0.5\n", "print(f'operational FALSE-POSITIVE RATE @0.5 = {fpr_op:.4f} ({fp:,} benign flagged of {fp+tn:,})')\n", "print('worst per-family recalls:', {k: round(v, 3) for k, v in srt.head(6).items()})" ] }, { "cell_type": "markdown", "id": "fc7a435f", "metadata": {}, "source": [ "## 10. Validity audit \u2014 is the score real?\n", "\n", "Three diagnostics. **(a)** How well can the *single best feature*, alone, separate the classes? A near-1.0 single-feature AUC means that feature is *near-sufficient* \u2014 a shortcut (which may be legitimate signal or an artifact), not the same as target leakage. **(b)** The exact-duplicate row rate. **(c)** The **train/test exact-row contamination** \u2014 the fraction of held-out rows that are duplicates of training rows, which is what actually inflates a held-out score. The trust grade is the *worse* of the single-feature and contamination concerns." ] }, { "cell_type": "code", "execution_count": 7, "id": "ae785c4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best single-feature AUC = 0.9879 (feature: DIntPkt)\n", " note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\n", " which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.\n", "exact-duplicate row rate (whole corpus) = 0.029\n", "TRAIN/TEST exact-row contamination = 0.019 (single-feat grade C, contam grade A)\n", "==> data trust grade: C (worse of the two; F = shortcut and/or heavy contamination)\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# --- Validity audit: is the score real detection, or a data shortcut? ---\n", "from sklearn.metrics import roc_auc_score\n", "samp = X.sample(min(60_000, len(X)), random_state=1); ysamp = y[samp.index]\n", "aucs = {}\n", "for c in feat: # AUC of EACH feature alone\n", " col = samp[c].to_numpy(float)\n", " if col.std()==0: continue\n", " a = roc_auc_score(ysamp, col); aucs[c] = max(a, 1-a) # direction-agnostic\n", "best_auc = max(aucs.values()); best_col = max(aucs, key=aucs.get)\n", "dup_rate = 1 - X.drop_duplicates().shape[0]/len(X) # exact-duplicate feature rows (whole set)\n", "# The statistic that actually inflates a held-out score is TRAIN/TEST CONTAMINATION: how many test\n", "# rows are exact duplicates of a training row. Measure it directly on the split used above.\n", "_trkeys = set(map(tuple, np.round(Xtr.to_numpy(), 6)))\n", "_te = np.round(Xte.to_numpy(), 6)[:50_000]\n", "contam = float(np.mean([tuple(r) in _trkeys for r in _te])) # fraction of test rows seen in train\n", "# Trust grade reflects BOTH failure modes and takes the WORSE of the two: a near-perfect single\n", "# feature (shortcut) OR heavy train/test contamination each independently invalidate the headline.\n", "_ga = 'F' if best_auc>=0.999 else 'D' if best_auc>=0.99 else 'C' if best_auc>=0.95 else 'B' if best_auc>=0.85 else 'A'\n", "_gc = 'F' if contam>=0.5 else 'D' if contam>=0.3 else 'C' if contam>=0.15 else 'B' if contam>=0.05 else 'A'\n", "grade = max(_ga, _gc) # 'max' letter = worse grade (A best, F worst)\n", "print(f'best single-feature AUC = {best_auc:.4f} (feature: {best_col})')\n", "print(f' note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\\n'\n", " f' which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.')\n", "print(f'exact-duplicate row rate (whole corpus) = {dup_rate:.3f}')\n", "print(f'TRAIN/TEST exact-row contamination = {contam:.3f} (single-feat grade {_ga}, contam grade {_gc})')\n", "print(f'==> data trust grade: {grade} (worse of the two; F = shortcut and/or heavy contamination)')\n", "s = pd.Series(aucs).sort_values().tail(15)\n", "fig, ax = plt.subplots(figsize=(8,5))\n", "s.plot.barh(ax=ax, color=['#e76f51' if v>=0.99 else '#457b9d' for v in s]); ax.axvline(0.5,ls='--',c='grey')\n", "ax.set_xlim(0.5,1.0); ax.set_title('Single-feature ROC-AUC (red = near-perfect shortcut)'); ax.set_xlabel('AUC alone')\n", "plt.tight_layout(); plt.show()" ] }, { "cell_type": "markdown", "id": "856851f1", "metadata": {}, "source": [ "## 11. Ablation \u2014 does the headline survive removing the artifacts?\n", "\n", "Narrating a shortcut is not enough. We *retrain the winning model* after (1) de-duplicating the corpus (removing the train/test contamination) and (2) dropping the single strongest feature. We report the held-out AUC each time. **Read the result honestly, both ways:** if the AUC **collapses**, the headline was a contamination/shortcut artifact. If it **barely moves** \u2014 common on *simulated* corpora \u2014 that is **not vindication**. It means the classes are separable by *many* redundant features because the attack and benign distributions barely overlap. That is its own generation artifact. The numbers below decide which story is true here, not the prose." ] }, { "cell_type": "code", "execution_count": 8, "id": "a9d1a71d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ablation \u2014 how much of the headline survives once each artifact is removed:\n" ] }, { "data": { "text/html": [ "
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settingheld_out_auc
0headline (as-is)1.0
1de-duplicated (3% rows removed)1.0
2shortcut feature dropped (DIntPkt)1.0
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" ], "text/plain": [ " setting held_out_auc\n", "0 headline (as-is) 1.0\n", "1 de-duplicated (3% rows removed) 1.0\n", "2 shortcut feature dropped (DIntPkt) 1.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# --- Ablation: SHOW the inflation empirically, don't just narrate it ---\n", "from sklearn.base import clone\n", "def _retrain_auc(Xa, ya): # re-split, stratified-subsample, refit best family\n", " xtr, xte, ytr2, yte2 = train_test_split(Xa, ya, test_size=0.25, random_state=RANDOM_STATE, stratify=ya)\n", " if len(xtr) > 120_000:\n", " xtr, _, ytr2, _ = train_test_split(xtr, ytr2, train_size=120_000, random_state=RANDOM_STATE, stratify=ytr2)\n", " m = clone(best); m.fit(xtr, ytr2)\n", " return roc_auc_score(yte2, m.predict_proba(xte)[:, 1])\n", "base_auc = roc_auc_score(yte, best.predict_proba(Xte)[:, 1]) # (0) the headline held-out AUC\n", "Xdd = X.drop_duplicates(); ydd = y[Xdd.index] # (1) de-duplicated corpus\n", "auc_dedup = _retrain_auc(Xdd, ydd)\n", "auc_noshort = _retrain_auc(X.drop(columns=[best_col]), y) if best_col in X.columns else base_auc # (2) drop shortcut\n", "ablation = pd.DataFrame([\n", " {'setting': 'headline (as-is)', 'held_out_auc': round(base_auc, 6)},\n", " {'setting': f'de-duplicated ({1-len(Xdd)/len(X):.0%} rows removed)', 'held_out_auc': round(auc_dedup, 6)},\n", " {'setting': f'shortcut feature dropped ({best_col})', 'held_out_auc': round(auc_noshort, 6)},\n", "])\n", "print('Ablation \u2014 how much of the headline survives once each artifact is removed:')\n", "ablation" ] }, { "cell_type": "markdown", "id": "a333f3cb", "metadata": {}, "source": [ "## 12. Reproducibility & robustness" ] }, { "cell_type": "code", "execution_count": 9, "id": "685b92fe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n", "XGBoost 3-fold CV ROC-AUC = 1.0000 +/- 0.0000 (mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)\n" ] } ], "source": [ "# --- Reproducibility & robustness ---\n", "import sklearn\n", "from sklearn.model_selection import StratifiedKFold, cross_val_score\n", "print(f'seed={RANDOM_STATE} | numpy {np.__version__} | sklearn {sklearn.__version__} | '\n", " f'xgboost {xgb.__version__} | lightgbm {lgb.__version__}')\n", "# 3-fold cross-validated ROC-AUC of the winning model (fresh clone, bounded subsample) -> mean +/- std.\n", "from sklearn.base import clone\n", "cvX, cvy = Xtr.iloc[:40_000], ytr[:40_000]\n", "def _auc_scorer(est, Xv, yv): # robust to xgboost's 2-col predict_proba\n", " p = est.predict_proba(Xv)\n", " p = p[:, 1] if getattr(p, 'ndim', 1) == 2 else p\n", " return roc_auc_score(yv, p)\n", "try:\n", " cv = cross_val_score(clone(best), cvX, cvy,\n", " cv=StratifiedKFold(3, shuffle=True, random_state=RANDOM_STATE),\n", " scoring=_auc_scorer, error_score='raise')\n", " assert np.all(np.isfinite(cv)), 'non-finite CV folds' # FAIL CLOSED: never narrate a NaN as evidence\n", " print(f'{best_name} 3-fold CV ROC-AUC = {cv.mean():.4f} +/- {cv.std():.4f} '\n", " f'(mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)')\n", "except Exception as e:\n", " print(f'CV UNAVAILABLE ({type(e).__name__}: {str(e)[:60]}); rely on the single held-out AUC above \u2014 '\n", " f'we do NOT report a CV number we could not compute')" ] }, { "cell_type": "markdown", "id": "380e24df", "metadata": {}, "source": [ "## 13. Scientific conclusion\n", "\n", "Flow features separate benign from attack IIoT traffic well above the 93%-benign baseline. But that number is carried by the dominant DoS flood. The honest, safety-relevant metrics are per-family recall on the rare command-injection (CommInj) and Backdoor flows, plus the benign false-positive rate. That rate matters because a false trip can halt a physical process.\n", "\n", "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.9272**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **XGBoost** (3-fold CV ROC-AUC **1.0000**). Strongest *single* feature: `DIntPkt` at AUC **0.9879**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **1.000000 \u2192 1.000000**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication does **not** lower the score (**1.000000**). So duplicate rows are not what props it up. Train/test overlap is negligible too, so neither issue explains the result. Data-trust grade: **C**. It is the worse of two independent sub-checks. Single-feature AUC 0.9879 scores **C**. Train/test exact-row overlap 0.019 scores **A**. The single-feature check drives the grade, not the overlap check. Train/test overlap separately scores A, so overlap is not the issue here. Operational false-positive rate at threshold 0.5: **0.0000**. Worst per-group recalls, exactly as printed: {`Backdoor`: 0.96, `CommInj`: 0.971, `DoS`: 1.0, `Reconn`: 1.0}. The weakest group sits at **0.960**, which is where detection is thinnest. **Disclosed limitation:** categorical columns are integer-encoded before the split. The encoder therefore sees the test set's category values. On an all-numeric corpus that step is a no-op. The mapping never consults the label, so no *label* information leaks. It is still transductive. A deployed system would need an unseen-category bucket. **How the audit numbers are computed:** overlap is measured on the first 50,000 held-out rows, so read it as a sampled estimate. Each ablation re-splits and refits, so tiny differences are re-split noise. The de-duplication variant keeps the first label when a feature vector appears twice. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only." ] }, { "cell_type": "markdown", "id": "3e7e5806", "metadata": {}, "source": [ "## References\n", "\n", "1. Zolanvari, M., Teixeira, M.A., Gupta, L., Khan, K.M. & Jain, R. (2021). WUSTL-IIoT-2021 Dataset for IIoT Cyber-Security Research. *Washington University in St. Louis*.\n", "2. Zolanvari, M. et al. (2019). Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. *IEEE Internet of Things Journal*.\n", "3. Morris, T.H. & Gao, W. (2014). Industrial Control System Traffic Data Sets for Intrusion Detection Research. *Critical Infrastructure Protection VIII* (ICCIP 2014), IFIP AICT 441, Springer, 65\u201378.\n", "4. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*." ] } ], "metadata": { "kernelspec": { "display_name": "seas8414-phase-notebooks", "language": "python", "name": "seas8414-phase-notebooks" } }, "nbformat": 4, "nbformat_minor": 5 }