{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "51f80eb9",
   "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 OTIDS CAN frames, captured from a second vehicle.\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": "21a3bd68",
   "metadata": {},
   "source": [
    "# In-Vehicle CAN Intrusion Detection on OTIDS (Second Vehicle)\n",
    "### Model comparison + per-capture recall + validity audit (real CAN frames, \u22651M)\n",
    "\n",
    "**Abstract:** OTIDS (Lee, Jeong & Kim, 2017) is a **second** in-vehicle CAN-bus dataset from the HCRL lab. It was captured on a different vehicle than the nb32 Car-Hacking set. Crucially, it also includes an **impersonation** attack (a node masquerading as another ECU) that Car-Hacking lacks. Each frame is a CAN ID plus eight payload bytes, labelled normal or one of three attack captures (DoS, fuzzy, impersonation). We keep every attack frame and bound the normal ones to \u22651M, compare four learners, and audit \u2014 a cross-dataset companion to nb32."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ad98a8",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Classify a CAN frame as normal or attack on a second vehicle/dataset. The value over nb32 is the impersonation attack, which reuses legitimate arbitration IDs. That is the case where payload-only detection is weakest. It is also where the question of whether frame-level features generalize across vehicles becomes concrete."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7202fbc5",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Lee, Jeong & Kim (2017)** \u2014 *OTIDS: A Novel Intrusion Detection System for In-Vehicle Network by Using Remote Frame* (PST). The dataset and an offset-ratio / time-interval detector.\n",
    "- **Song, Woo & Kim (2020)** \u2014 the Car-Hacking dataset (nb32 companion).\n",
    "- **Koscher et al. (2010)** \u2014 *Experimental Security Analysis of a Modern Automobile* (IEEE S&P).\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",
    "| Lee et al. (2017) \u2014 offset-ratio / time-interval detection | uses request/response TIMING, not payload; complementary to a frame classifier |\n",
    "| Per-frame classifiers (our setting) | impersonation reuses valid IDs, so payload-only detection is weakest there |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89068de",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `bikashkundu/can-hcrl-otids` (HCRL OTIDS) |\n",
    "| Rows | 3,744,041 frames as loaded (printed below); every attack kept + normal capped |\n",
    "| Label | `target` 0 normal; 1/2/3 = the three attack captures |\n",
    "| Access | Kaggle API token required |\n",
    "\n",
    "**Honestly:** the three attack captures map to OTIDS's DoS, fuzzy and impersonation attacks. But we label them by capture id (`attack_capture_1/2/3`) rather than assert a file\u2192attack mapping we cannot verify. We keep all attack frames and subsample normal, so the attack rate is a bounded-sample rate. Timing is dropped (frames classified independently), which is exactly where impersonation is hardest to catch.\n",
    "\n",
    "**Why the loaded count is smaller than the archive:** the loader keeps every attack frame, then subsamples the normal ones. The cap is the `1_500_000` ceiling in the `norm = raw[raw['y'] == 0].sample(min(...), ...)` line. Normal frames above that ceiling never enter `df`. So the loaded total is a *capped* total, not the archive total. This notebook never prints the archive's own row count, so we do not quote one. One-line check after the loader cell: `print(f'{len(raw):,} raw -> {len(df):,} loaded')`.\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 `bikashkundu/can-hcrl-otids` -> `/tmp/kg_otids`. It is about **746 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": "4e48a449",
   "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": "bc3dd612",
   "metadata": {},
   "source": [
    "**Figure 4.1 \u2014 Solution design (methodology).**\n",
    "\n",
    "<img 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\" style=\"max-width:100%;height:auto;\" alt=\"Figure 4.1 \u2014 Solution design (methodology).\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f02bbe",
   "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": "9e4c681a",
   "metadata": {},
   "source": [
    "**Figure 5.1 \u2014 Implementation architecture.**\n",
    "\n",
    "<img 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\" style=\"max-width:100%;height:auto;\" alt=\"Figure 5.1 \u2014 Implementation architecture.\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f88b27c",
   "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": "a99202e0",
   "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": "a244bcc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 3,744,041 CAN frames x 11 features; attack rate 0.5994; families ['attack_capture_1', 'attack_capture_2', 'attack_capture_3', 'normal']\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "# OTIDS (Lee, Jeong & Kim, 2017; HCRL): a SECOND in-vehicle CAN dataset (different vehicle than the\n",
    "# Car-Hacking set in nb32) with DoS, fuzzy and IMPERSONATION attacks \u2014 the last is absent from\n",
    "# Car-Hacking. Pre-processed to TS, CAN ID, 8 payload bytes and a `target` code (0 normal; 1/2/3 =\n",
    "# the three attack captures). Self-contained Kaggle download.\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "DEST = '/tmp/kg_otids'; os.makedirs(DEST, exist_ok=True)\n",
    "if not glob.glob(DEST + '/**/dataset*.csv', recursive=True):\n",
    "    import kaggle; kaggle.api.authenticate()\n",
    "    print('downloading OTIDS (one-time)...')\n",
    "    kaggle.api.dataset_download_files('bikashkundu/can-hcrl-otids', path=DEST, unzip=True, quiet=True)\n",
    "files = sorted(glob.glob(DEST + '/**/dataset*.csv', recursive=True))  # dataset.csv (normal) + dataset1/2/3 (attacks)\n",
    "assert files, 'OTIDS dataset*.csv not found'\n",
    "parts = []\n",
    "for f in files:\n",
    "    d = pd.read_csv(f, low_memory=False); d.columns = [str(c).strip() for c in d.columns]\n",
    "    parts.append(d)\n",
    "raw = pd.concat(parts, ignore_index=True)\n",
    "raw['target'] = pd.to_numeric(raw['target'], errors='coerce').fillna(0).astype(int)\n",
    "raw['y'] = (raw['target'] != 0).astype(int)\n",
    "# Keep EVERY attack frame; bound the normal frames (they are the majority) so the set stays \u22651M.\n",
    "atk = raw[raw['y'] == 1]\n",
    "norm = raw[raw['y'] == 0].sample(min(int((raw['y'] == 0).sum()), 1_500_000), random_state=0)\n",
    "df = pd.concat([atk, norm]).reset_index(drop=True)\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'\n",
    "df['family'] = np.where(df['y'] == 0, 'normal', 'attack_capture_' + df['target'].astype(str))\n",
    "# Drop the timestamp + label; keep the CAN arbitration ID and the 8 payload bytes.\n",
    "DROP = ['TS', 'target', '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": "9a22dbe1",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e6baf42",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1320x480 with 2 Axes>"
      ]
     },
     "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": "406b9113",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x600 with 3 Axes>"
      ]
     },
     "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": "6d244a22",
   "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": "9a2ad30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 3,744,041 rows | trained on 120,000 (stratified subsample) | held-out 936,011\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.5994  (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": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>train_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.858807</td>\n",
       "      <td>0.954777</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.855056</td>\n",
       "      <td>0.952706</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.856261</td>\n",
       "      <td>0.951774</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.612609</td>\n",
       "      <td>0.603834</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.599400</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy   roc_auc  train_s\n",
       "0             XGBoost  0.858807  0.954777      0.4\n",
       "1            LightGBM  0.855056  0.952706      1.3\n",
       "2        RandomForest  0.856261  0.951774      0.9\n",
       "3  LogisticRegression  0.612609  0.603834      0.2\n",
       "4    MajorityBaseline  0.599400  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": "a633802c",
   "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": "f696d6ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zd/z65p9EAgAAAAAA3kAl/8ExAAAAAADwPMICAAAAAADInLAAAAAAAAAyJywAAAAAAIDMCQsAAAAAACBzwgIAAAAAAMicsAAAAAAAADInLAAAAAAAgMwJCwAAAAAAIHPCAgAAAAAAyJywAAAAAAAAMicsAAAAAACAzAkLAAAAAAAgc8ICAAAAAAD4lbe/hzmwanybvHoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1560x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operational FALSE-POSITIVE RATE @0.5 = 0.1586  (59,472 benign flagged of 375,000)\n",
      "worst per-family recalls: {'attack_capture_3': 0.809, 'attack_capture_1': 0.896, 'attack_capture_2': 0.944}\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": "68c3d47c",
   "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": "151aea2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.5773  (feature: ID1)\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) = 0.903\n",
      "TRAIN/TEST exact-row contamination       = 0.830  (single-feat grade A, contam grade F)\n",
      "==> data trust grade: F   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "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": "9ed82cda",
   "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.\n",
    "\n",
    "**Caveat \u2014 de-duplication resolves every label conflict to `attack`:** the ablation calls `X.drop_duplicates()`. That keeps the *first* occurrence of each feature vector. The loader built `df` as `pd.concat([atk, norm])`, so every attack frame sits above every normal frame. A vector that appears in both an attack capture and the normal capture therefore survives as the attack row. Its benign twins are deleted, not reconciled.\n",
    "\n",
    "The ambiguity is removed in one direction only. Those conflicted rows are exactly the ones no frame-level detector can get right. So the de-duplicated AUC below is an upper bound, not a corrected headline. Read it as *how easy the task becomes once conflicts are deleted*. Do not read it as *the headline with contamination removed*.\n",
    "\n",
    "The unbiased version would resolve each duplicate key by majority label, or drop conflicted keys outright, then refit. We do not run that here. The direction of the bias is read off the code. Its size is unmeasured. One-line check, after the next cell: `print(len(X.drop_duplicates()), len(X.assign(_y=y).drop_duplicates()))`. The first count is unique feature vectors. The second counts unique (vector, label) pairs. A larger second number means conflicted keys exist, and the difference is how many. Do *not* use the before/after attack rate for this. It also moves with within-class duplication, which a DoS flood dominates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19feb57c",
   "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": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>setting</th>\n",
       "      <th>held_out_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>headline (as-is)</td>\n",
       "      <td>0.954777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (90% rows removed)</td>\n",
       "      <td>0.996659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (ID1)</td>\n",
       "      <td>0.946496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            setting  held_out_auc\n",
       "0                  headline (as-is)      0.954777\n",
       "1  de-duplicated (90% rows removed)      0.996659\n",
       "2    shortcut feature dropped (ID1)      0.946496"
      ]
     },
     "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": "e0f57c99",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6fe51c35",
   "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": [
      "XGBoost 3-fold CV ROC-AUC = 0.9521 +/- 0.0008  (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": "9914560e",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "As with Car-Hacking (nb32), payload and ID features separate injected from legitimate frames well, and per-capture recall shows which attacks are easy. Because we drop timing, the offset-ratio / time-interval signal OTIDS was built around is absent. A timing/sequence model is one honest next step. Cross-vehicle transfer between this dataset and nb32 (unmeasured here) is the other (Lee et al., 2017; Sommer & Paxson, 2010).\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.5994**. 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 **0.9521**). Strongest *single* feature: `ID1` at AUC **0.5773**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.954777 \u2192 0.946496**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication **raises** the AUC, to **0.996659**. That is not evidence the headline is safe. Collapsing duplicates removes the hardest rows. Where identical feature vectors carry conflicting labels, the rule resolves them all to `attack`. The de-duplicated task is therefore *easier*, not cleaner. Data-trust grade: **F**. It is the worse of two independent sub-checks. Single-feature AUC 0.5773 scores **A**. Train/test exact-row overlap 0.830 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: **0.1586**. Worst per-group recalls, exactly as printed: {`attack_capture_3`: 0.809, `attack_capture_1`: 0.896, `attack_capture_2`: 0.944}. The weakest group sits at **0.809**, 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 row when a feature vector repeats. The loader stacks attack frames above normal ones. Any conflicting key therefore resolves to `attack`, which biases that row upward. It is an upper bound, not a corrected headline. How many keys conflict is not measured here. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b966c0a",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Lee, H., Jeong, S.-H. & Kim, H.K. (2017). OTIDS: A Novel Intrusion Detection System for In-Vehicle Network by Using Remote Frame. *15th Annual Conf. on Privacy, Security and Trust (PST)*, 57\u201366.\n",
    "2. Song, H.M., Woo, J. & Kim, H.K. (2020). In-Vehicle Network Intrusion Detection Using Deep CNN. *Vehicular Communications*, 21.\n",
    "3. Koscher, K. et al. (2010). Experimental Security Analysis of a Modern Automobile. *IEEE S&P*.\n",
    "4. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*."
   ]
  }
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