{ "cells": [ { "cell_type": "markdown", "id": "2233ceee", "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 real in-vehicle CAN frames from the Car-Hacking captures.\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", "\n", "---" ] }, { "cell_type": "markdown", "id": "38573ffd", "metadata": {}, "source": [ "# In-Vehicle CAN-Bus Intrusion Detection (Car-Hacking / HCRL)\n", "### Model comparison + per-attack recall + validity audit (real CAN frames, \u22651M)\n", "\n", "**Abstract:** The Car-Hacking dataset (HCRL; Song et al., 2020) is **real** in-vehicle CAN-bus traffic from a Hyundai YF Sonata, with message-injection attacks: **DoS**, **fuzzing**, **gear-spoofing** and **RPM-spoofing**. Each row is a single CAN frame \u2014 an arbitration ID plus up to eight payload bytes \u2014 flagged normal or injected. From ~16.6M frames we keep every attack frame and bound the normal ones to \u22651M, compare four learners, and report recall per attack type. A **new domain**: automotive / in-vehicle networks, where there is no authentication on the bus." ] }, { "cell_type": "markdown", "id": "4291fc8d", "metadata": {}, "source": [ "## 1. Research problem\n", "\n", "**Task:** Classify a CAN frame as legitimate or injected from its arbitration ID, DLC and payload bytes. The CAN bus has **no built-in authentication**, so any node can inject frames. The defensive question is whether a frame-level classifier catches injection \u2014 especially spoofing, which mimics legitimate IDs \u2014 without flagging normal traffic that would disrupt the vehicle." ] }, { "cell_type": "markdown", "id": "65a2d4b0", "metadata": {}, "source": [ "## 2. Literature review\n", "\n", "- **Song, Woo & Kim (2020)** \u2014 *In-Vehicle Network Intrusion Detection Using Deep Convolutional Neural Network* (Vehicular Communications): the dataset and a CNN IDS.\n", "- **Seo, Song & Kim (2018)** \u2014 *GIDS: GAN-based Intrusion Detection System for In-Vehicle Network*.\n", "- **Koscher et al. (2010)** \u2014 *Experimental Security Analysis of a Modern Automobile* (IEEE S&P): why the CAN bus is exposed.\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", "| Song et al. (2020) \u2014 deep CNN on CAN-image frames | uses frame sequences/timing; strong but heavier than a per-frame model |\n", "| Per-frame classifiers (our setting) | ignore inter-frame timing; spoofing that reuses valid IDs is the hard case |\n" ] }, { "cell_type": "markdown", "id": "4d344fdf", "metadata": {}, "source": [ "## 3. Dataset provenance & honesty caveats\n", "\n", "| Property | Value |\n", "|---|---|\n", "| Source | Kaggle `pranavjha24/car-hacking-dataset` (HCRL Car-Hacking) |\n", "| Rows | ~16.6M CAN frames; every attack kept + normal bounded to \u22651M |\n", "| Label | R (normal) vs T (injected); family = DoS/fuzzy/gear/RPM |\n", "| Access | Kaggle API token required |\n", "\n", "**Honestly:** each file targets one attack type, so we keep all attack frames and subsample normal. The attack rate below is a bounded-sample rate, not the on-bus base rate. We classify each frame **independently**, dropping the timestamp. The model therefore cannot use inter-frame **timing** \u2014 the strongest CAN-IDS signal for flooding attacks. The audit and per-family recall then put that in context.\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 `pranavjha24/car-hacking-dataset` -> `/tmp/kg_carhack`. It is about **902 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": "975892f5", "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": "20bb56d7", "metadata": {}, "source": [ "**Figure 4.1 \u2014 Solution design (methodology).**\n", "\n", "\"Figure" ] }, { "cell_type": "markdown", "id": "edb2a3ef", "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": "92818e0a", "metadata": {}, "source": [ "**Figure 5.1 \u2014 Implementation architecture.**\n", "\n", "\"Figure" ] }, { "cell_type": "markdown", "id": "28f841a9", "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": "a97d14cd", "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": "9f316868", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loaded 3,931,517 CAN frames x 10 features; attack rate 0.5930; families ['dos', 'fuzzy', 'gear', 'normal', 'rpm']\n" ] } ], "source": [ "import os, glob\n", "# Car-Hacking (HCRL; Song, Woo & Kim, 2020): real in-vehicle CAN-bus traffic from a Hyundai YF Sonata\n", "# with message-injection attacks \u2014 DoS, fuzzing, gear-spoofing, RPM-spoofing. Each row is ONE CAN\n", "# frame: arbitration ID + up to 8 payload bytes; a flag R/T marks injected frames. NEW domain:\n", "# automotive / in-vehicle networks. Self-contained Kaggle download.\n", "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n", "DEST = '/tmp/kg_carhack'; os.makedirs(DEST, exist_ok=True)\n", "if not glob.glob(DEST + '/**/*.csv', recursive=True):\n", " import kaggle; kaggle.api.authenticate()\n", " print('downloading Car-Hacking dataset (one-time)...')\n", " kaggle.api.dataset_download_files('pranavjha24/car-hacking-dataset', path=DEST, unzip=True, quiet=True)\n", "files = sorted(glob.glob(DEST + '/**/*.csv', recursive=True))\n", "frames = []\n", "for f in files:\n", " atype = os.path.basename(f).split('_')[0].lower() # dos / fuzzy / gear / rpm\n", " d = pd.read_csv(f, header=None, low_memory=False, dtype=str)\n", " isatk = (d == 'T').any(axis=1) # a frame is attack iff any cell == 'T'\n", " d = d.iloc[:, :11].copy()\n", " d.columns = ['ts', 'can_id', 'dlc', 'b0', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7']\n", " d['y'] = isatk.astype(int); d['family'] = np.where(isatk, atype, 'normal')\n", " atk = d[d['y'] == 1] # keep EVERY injected frame\n", " norm = d[d['y'] == 0].sample(min(int((d['y'] == 0).sum()), 400_000), random_state=0) # bound normal\n", " frames.append(pd.concat([atk, norm]))\n", "df = pd.concat(frames, ignore_index=True)\n", "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'\n", "def _hex(x):\n", " try: return int(str(x), 16)\n", " except Exception: return 0 # 'R'/'T' or NaN bytes in short frames -> 0\n", "for c in ['can_id', 'b0', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7']:\n", " df[c] = df[c].map(_hex)\n", "df['dlc'] = pd.to_numeric(df['dlc'], errors='coerce').fillna(0)\n", "# Drop the timestamp (identifier). Keep the arbitration ID, DLC and the 8 payload bytes.\n", "DROP = ['ts', 'y', 'family']\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):,} CAN frames x {len(feat)} features; attack rate {y.mean():.4f}; families {sorted(set(family))}')" ] }, { "cell_type": "markdown", "id": "a5fd43e3", "metadata": {}, "source": [ "## 7. Exploratory data analysis" ] }, { "cell_type": "code", "execution_count": 3, "id": "aaf7ded7", "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": "ed9572de", "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": "53e0585e", "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": "fe38fe64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "materialized 3,931,517 rows | trained on 120,000 (stratified subsample) | held-out 982,880\n", "MAJORITY-CLASS BASELINE accuracy = 0.5930 (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
0RandomForest0.9999951.0000000.4
1XGBoost0.9999591.0000000.3
2LightGBM0.9999130.9999951.2
3LogisticRegression0.8523950.8714940.2
4MajorityBaseline0.5930000.5000000.0
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" ], "text/plain": [ " model accuracy roc_auc train_s\n", "0 RandomForest 0.999995 1.000000 0.4\n", "1 XGBoost 0.999959 1.000000 0.3\n", "2 LightGBM 0.999913 0.999995 1.2\n", "3 LogisticRegression 0.852395 0.871494 0.2\n", "4 MajorityBaseline 0.593000 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": "4b716485", "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": "0cb013a9", "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 (3 benign flagged of 400,000)\n", "worst per-family recalls: {'fuzzy': 1.0, 'rpm': 1.0, 'gear': 1.0, 'dos': 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": "2fd8a171", "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": "9b66412e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best single-feature AUC = 0.7113 (feature: b3)\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) = 0.853\n", "TRAIN/TEST exact-row contamination = 0.811 (single-feat grade A, contam grade F)\n", "==> data trust grade: F (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": "789326b1", "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": "b4a7fb33", "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 (85% rows removed)1.0
2shortcut feature dropped (b3)1.0
\n", "
" ], "text/plain": [ " setting held_out_auc\n", "0 headline (as-is) 1.0\n", "1 de-duplicated (85% rows removed) 1.0\n", "2 shortcut feature dropped (b3) 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": "3bb168a3", "metadata": {}, "source": [ "## 12. Reproducibility & robustness" ] }, { "cell_type": "code", "execution_count": 9, "id": "628942f5", "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 = 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": "e626e14c", "metadata": {}, "source": [ "## 13. Scientific conclusion\n", "\n", "Frame-level features (arbitration ID and payload bytes) separate injected from legitimate CAN traffic essentially perfectly, and every attack family is recovered. Injected frames simply carry anomalous payloads. Two honest caveats frame that result. **(1)** The audit flags grade-F contamination because CAN frames repeat heavily, so a random split leaks. But the ablation shows the score is **not** an artifact of it: de-duplicating removes ~85% of rows and the AUC stays ~1.0. The separability is real; the grade-F is a warning that a random split over repetitive frames is the wrong protocol, not that the score is fake. **(2)** We classify frames independently and dropped timing, so the easy win here is payload anomaly. The genuinely hard case \u2014 masquerade/replay attacks that reuse valid IDs *and* plausible payloads \u2014 needs inter-frame **timing**, i.e. a sequence model over the frame stream. That is the direction the CAN-IDS literature takes: Song et al. (2020) classify frame *sequences* with a CNN, and Lee et al. (2017) detect intrusions from request/response **time intervals** rather than payload content.\n", "\n", "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.5930**. 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 **1.0000**). Strongest *single* feature: `b3` at AUC **0.7113**. 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. The overlap above is a caveat about the *random split*, not proof the score is fabricated. Data-trust grade: **F**. It is the worse of two independent sub-checks. Single-feature AUC 0.7113 scores **A**. Train/test exact-row overlap 0.811 scores **F**. The overlap check drives the grade, not the single-feature check. That says the split leaks, not that features are clean; the single-feature check separately scores A. On this A-best / F-worst scale, a D or F means the headline is optimistic. Treat it as a benchmark number, not a deployment estimate. Operational false-positive rate at threshold 0.5: **0.0000**. Worst per-group recalls, exactly as printed: {`fuzzy`: 1.0, `rpm`: 1.0, `gear`: 1.0, `dos`: 1.0}. Every group listed is recovered essentially perfectly. So there is no rare-class failure to report on this split. That uniformity suggests the corpus is easy to separate, not that the detector is strong. **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": "f2d4cd11", "metadata": {}, "source": [ "## References\n", "\n", "1. Song, H.M., Woo, J. & Kim, H.K. (2020). In-Vehicle Network Intrusion Detection Using Deep Convolutional Neural Network. *Vehicular Communications*, 21.\n", "2. Seo, E., Song, H.M. & Kim, H.K. (2018). GIDS: GAN-based Intrusion Detection System for In-Vehicle Network. *PST*.\n", "3. Lee, H., Jeong, S.H. & Kim, H.K. (2017). OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame. *PST*, 57\u201366.\n", "4. Koscher, K. et al. (2010). Experimental Security Analysis of a Modern Automobile. *IEEE S&P*.\n", "5. 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 }