{ "cells": [ { "cell_type": "markdown", "id": "fae38035", "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 SWaT water-treatment testbed, a second and physically different ICS.\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 recall for each label group, not just accuracy. Here that group is `attack` alone.\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": "707d7b24", "metadata": {}, "source": [ "# Cyber-Physical Intrusion Detection on the SWaT Water-Treatment Testbed\n", "### Model comparison + validity audit: 120,000 training rows, 360,430 held out of 1,441,719 ICS samples (second ICS testbed)\n", "\n", "**Abstract:** SWaT (Goh et al., 2016) is the SUTD **Secure Water Treatment** testbed. It is a real six-stage water-treatment plant instrumented with 51 sensor/actuator signals. It ran for 11 days with staged cyber-attacks on the physical process. It is a **second, physically different** ICS testbed to HAI (nb31): where HAI combines a turbine, boiler and water process, SWaT is a single multi-stage water plant. We load 1,441,719 one-second samples. Four learners train on a 120,000-row stratified subsample. They are scored on the 360,430-row held-out split. We then audit that result \u2014 the same honest questions as HAI, on a different cyber-physical system." ] }, { "cell_type": "markdown", "id": "d5045a64", "metadata": {}, "source": [ "## 1. Research problem\n", "\n", "**Task:** Flag a one-second SWaT snapshot as normal or under-attack from physical process signals (flow, level, pressure, valve/pump states). Attacks are ~4% and manipulate the physical process. A point-in-time classifier is a first line. But the honest challenge is subtle single-point manipulations without false trips that would disrupt water treatment." ] }, { "cell_type": "markdown", "id": "ea893eaa", "metadata": {}, "source": [ "## 2. Literature review\n", "\n", "- **Goh, Adepu, Junejo & Mathur (2016)** \u2014 *A Dataset to Support Research in the Design of Secure Water Treatment Systems* (CRITIS): the SWaT testbed and dataset.\n", "- **Mathur & Tippenhauer (2016)** \u2014 *SWaT: A Water Treatment Testbed for Research and Training on ICS Security* (CySWater).\n", "- **Kravchik & Shabtai (2018)** \u2014 CNN anomaly detection on SWaT.\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", "| Kravchik & Shabtai (2018) \u2014 1D-CNN on SWaT | temporal/residual model; point-in-time classifiers are weaker |\n", "| Point-in-time classifiers (our setting) | ignore process dynamics; attacks are ~4% so accuracy is inflated |\n" ] }, { "cell_type": "markdown", "id": "f475a431", "metadata": {}, "source": [ "## 3. Dataset provenance & honesty caveats\n", "\n", "| Property | Value |\n", "|---|---|\n", "| Source | Kaggle `vishala28/swat-dataset-secure-water-treatment-system` (SUTD SWaT) |\n", "| Rows | 1,441,719 one-second samples (normal + attack merged) |\n", "| Label | `Normal/Attack` (~3.8% attack) |\n", "| Access | Kaggle API token required (original SWaT is request-gated via iTrust) |\n", "\n", "**Honestly:** the merged label is binary (no per-scenario attack type in this file), so per-family recall reduces to attack recall. Attacks are ~4%, so accuracy is inflated and the false-positive rate is the metric that matters. We classify each second independently, **ignoring temporal dynamics** \u2014 the signal SWaT anomaly detectors rely on \u2014 which we flag as the honest next step.\n", "\n", "### Before you run this: getting the data\n", "\n", "**Read this section in full. For this notebook the data question is part of the assignment.**\n", "\n", "**The correct way to obtain SWaT is from iTrust, not from Kaggle.** SUTD's iTrust\n", "centre releases SWaT through a request form. The download link is sent to an\n", "academic email address, and the terms require you to credit iTrust and\n", "**forbid you from sharing the dataset with anyone else**, publicly or privately.\n", "Every additional person must submit their own request. Use your GWU address:\n", "`https://itrust.sutd.edu.sg/itrust-labs_datasets/`\n", "\n", "**What the loader below actually reads is a third-party Kaggle mirror**\n", "(`vishala28/swat-dataset-secure-water-treatment-system`), and the stored outputs\n", "in this notebook were computed from it. Under the terms above, nobody is\n", "permitted to have published that mirror. That is not a footnote. It is your first\n", "finding under criterion 1: a corpus whose provenance you cannot verify is a\n", "corpus whose results you cannot verify.\n", "\n", "**If you obtain the original from iTrust**, it arrives as two workbooks, one\n", "normal-operation and one attack. The mirror's `merged.csv` is those two\n", "concatenated, normal first, written out with pandas. It is *not* a byte\n", "concatenation - the pandas round trip rewrites integer-looking values such as\n", "`2` as `2.0`, so `cat` will not reproduce it:\n", "\n", "```python\n", "import pandas as pd\n", "normal = pd.read_excel('SWaT_Dataset_Normal_v1.xlsx', header=1)\n", "attack = pd.read_excel('SWaT_Dataset_Attack_v0.xlsx', header=1)\n", "pd.concat([normal, attack], ignore_index=True).to_csv('/tmp/kg_swat/merged.csv', index=False)\n", "```\n", "\n", "**Then check these invariants against what this notebook printed.** They were\n", "verified against the mirror, so any disagreement is a real difference between the\n", "mirror and the original:\n", "\n", "| Invariant | Expected |\n", "|---|---|\n", "| Normal workbook rows | 1,387,098 |\n", "| Attack workbook rows | 54,621 |\n", "| Combined rows | 1,441,719 |\n", "| Columns | 53, last one named `Normal/Attack` |\n", "| Attack rate | 0.0379 |\n", "| Header quirk | leading spaces survive on ` Timestamp`, ` MV101`, ` AIT201` |\n", "\n", "If your numbers match, the mirror is faithful and the results here stand. If they\n", "do not, you have found something more interesting than anything in the rubric,\n", "and it belongs in your two minutes.\n", "\n", "**If iTrust approval does not arrive in time**, say so in your review and grade\n", "what you can. An honest \"I could not lawfully obtain this data, so criterion 1 is\n", "unresolved\" is a better answer than a score computed on a corpus you cannot\n", "account for." ] }, { "cell_type": "markdown", "id": "002edcb4", "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": "4c9b9bbb", "metadata": {}, "source": [ "**Figure 4.1 \u2014 Solution design (methodology).**\n", "\n", "\"Figure" ] }, { "cell_type": "markdown", "id": "42ef215d", "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": "dea6a817", "metadata": {}, "source": [ "**Figure 5.1 \u2014 Implementation architecture.**\n", "\n", "\"Figure" ] }, { "cell_type": "markdown", "id": "fd7f02a7", "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": "edf2f4c2", "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": "d531943d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loaded 1,441,719 SWaT samples x 46 sensor/actuator signals; attack rate 0.0379\n" ] } ], "source": [ "import os, glob\n", "# SWaT (Goh et al., 2016): the SUTD Secure Water Treatment testbed \u2014 a real 6-stage water-treatment\n", "# plant with 51 sensor/actuator signals, run 11 days with staged cyber-attacks. A SECOND, physically\n", "# DIFFERENT ICS testbed than HAI (nb31), enabling a cross-testbed contrast. Self-contained download.\n", "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n", "DEST = '/tmp/kg_swat'; os.makedirs(DEST, exist_ok=True)\n", "if not glob.glob(DEST + '/**/*.csv', recursive=True):\n", " import kaggle; kaggle.api.authenticate()\n", " print('downloading SWaT (one-time)...')\n", " kaggle.api.dataset_download_files('vishala28/swat-dataset-secure-water-treatment-system', path=DEST, unzip=True, quiet=True)\n", "cands = [x for x in glob.glob(DEST + '/**/*.csv', recursive=True) if os.path.basename(x).lower() == 'merged.csv']\n", "f = cands[0] if cands else 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", "LABEL = [c for c in df.columns if c.replace(' ', '').lower() in ('normal/attack', 'label')][0]\n", "df['y'] = (df[LABEL].astype(str).str.strip().str.lower() != 'normal').astype(int)\n", "df['family'] = np.where(df['y'] == 1, 'attack', 'normal') # merged label is binary (no per-scenario type)\n", "# Drop the label and the timestamp; keep the 51 physical sensor/actuator signals.\n", "DROP = [LABEL, 'y', 'family', 'Timestamp', 'timestamp']\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):,} SWaT samples x {len(feat)} sensor/actuator signals; attack rate {y.mean():.4f}')" ] }, { "cell_type": "markdown", "id": "0b9e2935", "metadata": {}, "source": [ "## 7. Exploratory data analysis" ] }, { "cell_type": "code", "execution_count": 3, "id": "9a08882e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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": "14e94cca", "metadata": {}, "outputs": [ { "data": { "image/png": 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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": "5aa90064", "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": "3e2faa83", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "materialized 1,441,719 rows | trained on 120,000 (stratified subsample) | held-out 360,430\n", "MAJORITY-CLASS BASELINE accuracy = 0.9621 (any model must beat THIS, not 0.5, to be interesting)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "best model: RandomForest\n" ] }, { "data": { "text/html": [ "
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modelaccuracyroc_auctrain_s
0RandomForest0.9990100.9999790.9
1XGBoost0.9991340.9999630.5
2LightGBM0.9990510.9999581.2
3LogisticRegression0.9902450.9788890.2
4MajorityBaseline0.9621000.5000000.0
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" ], "text/plain": [ " model accuracy roc_auc train_s\n", "0 RandomForest 0.999010 0.999979 0.9\n", "1 XGBoost 0.999134 0.999963 0.5\n", "2 LightGBM 0.999051 0.999958 1.2\n", "3 LogisticRegression 0.990245 0.978889 0.2\n", "4 MajorityBaseline 0.962100 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": "efc7edb8", "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": "7192e4a6", "metadata": {}, "outputs": [ { "data": { "image/png": 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y3GtmmI4/6TXTx1599dUMu87af7K/b6XjY5qhplliCatRkRkG3BmZYUgVa91c+zUm9Dtba/snnCmsszgc0ZkSb775ppkpobNCE9JZnDqT1J5mM33yySdmgXArnYGsM2B0ZoijdQI0I01nudrT2SQ6u0KzaDT7JuGaCzrzVM954sQJW4aU1dChQ6Vo0aLxsp505qzORNVjdV0CK8280tnKer10lm3BggUTvc6RI0cm2qbXTGcvW2fOKn3dOmPESheJ15nXOltEM7p0tok9XXdB3w/NNLKns2n0fdPntX8dSq+3vhadYaIznO0z9hwtlp7cQvVJ0euQGrr+g/rhhx8S1ZPWRdPtP296rXSGlGYd2r9uve76vulsGb1WOqMm4fX+z3/+E2+bZpjpe2//WUsvjq6tzsC2n/XsiH72lyxZYtqps6Ds6WvUz7fOKAoJCTGzte/m/y8AQObx559/mgXatQ+hGdP2dBauulNmjX53aVazzmrVtaK0f6YZy9qn0u9m7ZelhevXr5uMb+13Jdcf0f6D0ixzawZV4cKFTQa4ZqtpNlpasF6fhN+tKTFx4kQ5duxYivfXTChrdnpa++eff8xPXQvOEV0s/sCBA+am673d6Rh9z/UzoDOijxw5YusnJ3eM9u30PdV+jmb465ok+n6fPn3a9IUT/q1hbZfSdjmis9Rnz55t+qGVK1dO8fUAAMAVxt30e1O/x3TcTdfjTDjOpn/L16pVK942rZyk4wGatZ1wvECzt3U8ScfjtAqRtbKPdfxFM80SjhcpXbMzJe52/Ohexur++9//xtumfRDt/61Zs8aMcSTs296JVoSyvgfaJn1e7SdrP0LHlzLqOuu4oqPrruNjOq6q10Ur/GT0uvaAuyMYhlRJWH5EAxBaesQ60GBPv7h1IEJLxmjZHU3X1nRhK/3D1hH7MitWmjasLl++bNumachKS8VpcCQhLWuXMBi2fft281PTmBPSL0z9EtFBCT13wi9YR+3ShcqVo5J21i82/YPeEU0XT67MnrZVAyP6OhLSASV9zdZrYE/P6Sj4tmHDBvNTr4l+YSYUGhpqyjrqYIK+Hg2OabkaLT+jX+AaJNISP9oBuJtFyFOzwKoOfPz999+mvGPCjl1CGrzTwKxeb0fPYX2vHV0r7ZA4+uzo5816vdKDXkN97qlTp5qyTRq81c+xfsZ0IPJOrK9FO4kJ0/Str1k7ZbpfwmBYSv//AgB4rtRMbtJyLPbfFcmx7qOlXuyP1bI4OiiQFrT/qP0VR4ER+36EDjzoYEFwcLCtn/f000+bwQct19inT580aY/1NaZ0kChhMCxhXzU52v9Lr2CYtaxSUgvaW7drOaD0PkbfP91Pg2F38xz2NACqdPCMYBgAwJ3G3XTcRSdh69/9Wka4e/fuifZ1FOjR8R7tK+nx1j6fPe3/JZywrEtm6P53W375XseP0nqsztEYh6NJSDrelnDMTftmCftnOn6jQTL7/khGXGedVDRixAgzcU37nFp60l5S46oAkkYwDHdVp1j/SNVggX4h67pDOjsl4ZeWZkbp4Iqux6SD/frHqM5sUF9//XWS60LYrzNl+6D+m+2jXzRW1j+OrethJfXHrz3rMUkNoFi3O/qD2tEf4dZ2JfeY9QswtbStuhaZo+CInlvXdtIAVkpet3XWitIv0uRcu3bN/NT3VGfR6Jf6okWLTJaRtVOh66dZ18dID9br72imTFq+p44+a9brax+4TWsagNPa1VonWtfbsGZIakbes88+Kx9//HG8bMD0fs2O/v8CAHiu1ExucjXW/kxyWdQ6EUsny7z00kvxgnAaSNJgmK6xkFbBsHuhgypIX9qXVhcuXOBSAwDcQkrXWU9q/MfaV9JgjaOJ0AnHfqxjB9q3cpTZlRL3On6UEWMcSU1CShgM0zW99HXomJAGm3Sy2KhRo+Txxx+XhQsX2ta/Te/rrIEzHWeNiYmxVXPSygb6/FolQbPpUrreLoD/IRiGu6JpzlouR8vq6SKROoCv6ds6g1Nt3brVBMJ0H/2ysC9dp18ouhD3vbIGoM6fP+/w8XPnziV5jKPH7Gf3JjX7NCNpGzQlW4NpCbN/9MtQ/6h3VOInqVk31teknYyUlgbSkjZaTkifb9euXbJs2TKT1q2LeOpnQIOh6cHamUnJLBdXeE+tnSG9TgklNVNZO0BapklvOitfO2WaSTl69GhzjC4y68qvGQCQOSY36SCLZrNrycM7ZXnrPvaZ89aBCx0s0JmsaZEdZh08SDgz1t7YsWPNz4RZVFWrVjXZ77oYu/ZV7WcSW7/Lk5sMY33Muq/9a3T3mbn2/URHrNvtB5zu9hjtw+pjmjGY1DHWc9/Nc9i7efOm+Xm3g3sAALgyR+M/1u9OLauoS0mkhH6Pan9Nvzfv9jvzXsaPMmKMI7WTkLS/p8E8XWpEqx7oRGYdr7EG9tL7Og8fPtzst3LlykQBO51ArcEwAKn3v7/kgLtQvXp1efHFF00pQB3Ut9LBfaUzFxKuXaWzRax/mN4La/k8XavBUUaLoy866zGOHtMvbK0prDTA52zaVh100XTohHSbvubUtLNBgwbmp/U1poa+hzp4pBlMWtpPad1qK2upwbTKLNKOkg5YaaDTUXlDe5pNpWtX6CDUwYMHEz2uHYf0fk+ts9Otg4D2dLDtTsqWLWs6hhoQ04ywO3Vq7D/7jgJwGfGaAQCeNblJv8N1cpOu1WRPy/gqHdBIjpah0SCT0rI4SgcQtJSNflc56s/cDWspaOts3IR2795tWwOzYcOGZpDI/mZtozVgZmUd0EjqvPbZRfaBF+v1Wb58eapfi85Q1pnHKb3p/umlQoUKya69Ze1j2a/1ldwx+p5rEFX7kFolIiXH6ECXBmi15KR1gp1+RrVSgM6qtg6E3ald9qzvp6MS4gAAeCItnaiBnNSM/eh4kU6W0qyue3Wn8SNHXH2sTisLaKUrre4TERGRIddZx1U1w93R0impKbMNID6CYbhn//nPf8yXgqYOW+vxWtfCSvhFpmX9tIZwWtA/lFu1amX+0NbZGfY0mODoy6Fz587my0S/kDXl2J6WbtRz6aBQwhrEzqALYqrBgwfHG5jS399++23ze2oys1599VWTYaazVhwNQERFRcX7EtfBIkezcK2ZeNZBCmWd2avrMSRFFxzVW0pZZ9toiaOE7dAgof2AiF4r7VD83//9X7yAnA5affDBB7Z90ovOLNdOkK5PYv9eaWZfwoVUlX7OdDH5hPT/H01zv9MMIetnX+td6+fW3qZNm0w7NEBnXScFAIC7mdykXnjhBfPzp59+SjIbX2k/UL/DtB9VqlSpeAu7W2e33qkEcUpKvWgmVoECBUxFAkesQS4dONB+kqObfs9qX9C+bE2NGjXMz6TWDNWBGOsEF+u+qmvXrqZvqcfdKWCY8PVpcEtLVqb0lp7BMGtGoKPBGe2zaN9RMwftA1vJHaPBT+0TNWrUyFYm/U7HaDUJ+33u5Rgr7XtqH61atWpJvHIAADyLTgDRdVK136LjIY4mLR8+fNiMS1i99tpr5udbb73lMNv9ThnwqRk/csTVx+r0ebWvrJNsNDCWEddZx1V1TEknetnT0ua6LhuAu2QBUkA/Ksl9XF5//XXz+Ntvv23ux8TEWB544AGzrWHDhpb/+7//s/To0cNSoEABS+PGjS1BQUGWEiVKxDvHhAkTzP76M6k2NGvWLN62AwcOWPLly2cea9u2rWXw4MGWxx57zOLr62vp0KGDw/PNnj3b4ufnZ8mSJYvl6aefNsc8/PDDZt/ChQtbDh8+HG//Z5991jx29OjRRG169913zWMrV65M9FhSr0dfd1LnS+jxxx83+5YsWdLSv39/yxtvvGEpVaqU2datW7cUXSN7kydPNq9dr0/79u0tb775puW1116zdOrUyZI3b15LhQoV4r2nWbNmtTz00EOWl156yby3em11m7+/v2X9+vW2fS9dumTJli2bJVeuXJZXXnnF8sEHH5hbeHh4vLal5p+cuLg4yzPPPGOO0c/NCy+8YN4rfT+KFStmrr1VZGSk+VzpvlWqVDGfN21HwYIFzbaBAwfGO7dee92u53JEr6GjtiZ3fa1tLVeunHmvnn/+efN5sr6H9sfNmjXL4uXlZalXr55pg74ufX36OnXfL7744o5t1c+pnl8fa9WqlTlH9+7dzXuj77F+zu/1/y8AgGdJ7rv41KlT5jskMDDQfK87+o67//77LSdPnkx07HfffWfx8fGx5MiRw7Jnz554j12/ft1So0YNc7z2uy5fvpzo+KtXr1ree+89y/Dhw1P0Oh599FFzvoMHD8bbfuPGDdN+bcvp06eTPF6/L/X4sWPH2rbpuayvYffu3Un2+Zo3b57osV9++cU8pn2pRYsWOXzODRs2WGrVqmVJT9bv+nfeeSfJffT92Ldvn+X48ePxtmvfvVKlSub4OXPm2LbHxsZaunbtarZ//PHH8Y65cuWKJX/+/KZPvWXLFtv2mzdvmv6/HjN16tR4xxw5csR8zvRa2feF9TNXpkwZc4x9H1OtW7fObNfH7T+beryeR8/nqF9969Yt07Y6derc4coBAOB8qRkzSW4syvod3aBBA9sYxXPPPWfGdHRcrm7dug6/o//zn/+Y7Tlz5jR9vyFDhlh69eplxonsxyP0OXU/+zGZ1IwfJTU2kZZjdY7amNJrmtQxZ86csQQEBJjrExYWlu7XeeHChbb99HEdv2vatKnF29vb1jdzNN6Y2rFWILMhGIY0+VI+d+6cCYboTX9XFy9etPTp08f8Q6xffqVLlzZfZvpH+N38A53UYL0OXuigSO7cuc3z6xfRvHnzkj3f5s2bLZ07dzZ/wOuXrQZXXn75ZYcDJ84MhukAxLfffmv+iNcvXb3Vrl3bMnr0aPNYSq+RPR3g0ddUvHhx08nIkyePCSD17t3bsnz5ctt+GzduNNekevXqZh/txOggRM+ePS1//fVXovPqF7Ve++zZs9s+L/avMbX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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "operational FALSE-POSITIVE RATE @0.5 = 0.0002 (63 benign flagged of 346,775)\n", "worst per-family recalls: {'attack': 0.978}\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": "ba0ba060", "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": "ffad44ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best single-feature AUC = 0.9002 (feature: PIT503)\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) = 0.356\n", "TRAIN/TEST exact-row contamination = 0.062 (single-feat grade B, contam grade B)\n", "==> data trust grade: B (worse of the two; F = shortcut and/or heavy contamination)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "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": "3e4a216f", "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 \u2014 which is its own generation artifact. The numbers below decide which story is true here, not the prose." ] }, { "cell_type": "code", "execution_count": 8, "id": "d24255ac", "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
settingheld_out_auc
0headline (as-is)0.999979
1de-duplicated (36% rows removed)0.999987
2shortcut feature dropped (PIT503)0.999981
\n", "
" ], "text/plain": [ " setting held_out_auc\n", "0 headline (as-is) 0.999979\n", "1 de-duplicated (36% rows removed) 0.999987\n", "2 shortcut feature dropped (PIT503) 0.999981" ] }, "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": "b4420774", "metadata": {}, "source": [ "## 12. Reproducibility & robustness" ] }, { "cell_type": "code", "execution_count": 9, "id": "dbb848ca", "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "RandomForest 3-fold CV ROC-AUC = 0.9980 +/- 0.0006 (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": "3195433a", "metadata": {}, "source": [ "## 13. Scientific conclusion\n", "\n", "As on HAI, point-in-time process features separate normal from attack SWaT snapshots above the ~96%-normal baseline. But that number is meaningless at ~4% attacks. So attack recall and the false-positive rate (a false trip can disrupt water treatment) are the honest metrics. The structural limit is identical to HAI's. Classifying each second independently ignores the **temporal dynamics** of the physical process. So a residual/sequence model over time is the honest next step. And cross-testbed transfer (SWaT\u2194HAI), which neither notebook measures, is the real generalization question for cyber-physical IDS (Goh et al., 2016; Sommer & Paxson, 2010).\n", "\n", "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.9621**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **RandomForest** (3-fold CV ROC-AUC **0.9980**). Strongest *single* feature: `PIT503` at AUC **0.9002**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.999979 \u2192 0.999981**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication does **not** lower the score (**0.999987**). So duplicate rows are not what props it up. Overlap is not heavy, but it is **not negligible either** (grade B). The random split still flatters the headline a little. Data-trust grade: **B**. It is the worse of two independent sub-checks. Single-feature AUC 0.9002 scores **B**. Train/test exact-row overlap 0.062 scores **B**. Both sub-checks land on the same grade. Operational false-positive rate at threshold 0.5: **0.0002**. Worst per-group recalls, exactly as printed: {`attack`: 0.978}. The weakest group sits at **0.978**, 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": "9838d346", "metadata": {}, "source": [ "## References\n", "\n", "1. Goh, J., Adepu, S., Junejo, K.N. & Mathur, A. (2016). A Dataset to Support Research in the Design of Secure Water Treatment Systems. *CRITIS*.\n", "2. Mathur, A.P. & Tippenhauer, N.O. (2016). SWaT: A Water Treatment Testbed for Research and Training on ICS Security. *CySWater*.\n", "3. Kravchik, M. & Shabtai, A. (2018). Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks. *CPS-SPC*.\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 }