{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8c36d1f",
   "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 N-BaIoT device behaviour snapshots from nine commercial IoT devices.\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",
    "5. Exclude metadata files that are not measurements.\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 2: Service Enrichment and Device Fingerprinting** \u2014 Learning objective 3 (section 2.1) compares evidence sources by strength and **spoofability**. The features here are damped-window traffic statistics, not protocol fields. An attacker shifts them only indirectly, by shaping traffic timing and volume. That is a weaker form of the same spoofability concern.\n",
    "- **Chapter 10: Active Deception & Threat Hunting** \u2014 Learning objective 6 (section 10.1) places a claim on the **attribution ladder** and corrects for **dependence among rule hits**. The same rule stops us equating one feature with the label.\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": "0d363622",
   "metadata": {},
   "source": [
    "# IoT Botnet Detection on N-BaIoT (Mirai / BASHLITE Device Behaviour)\n",
    "### Model comparison + per-botnet-family recall + validity audit (7,062,606 behaviour snapshots)\n",
    "\n",
    "**Abstract:** N-BaIoT (Meidan et al., 2018) captures **behaviour snapshots** \u2014 damped-window traffic statistics \u2014 from 9 commercial IoT devices. Nine of them were infected in the lab with the **Mirai** and **BASHLITE (gafgyt)** botnets. With **7,062,606** snapshots across 115 features, we compare four learners and report recall **per botnet family** (mirai.udp, gafgyt.combo, \u2026). The label is encoded in the filename, not a column, so the loader recovers it explicitly. A device-behaviour angle on the IoT-botnet problem, distinct from the flow-based IoT-23 (nb19)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346c9b9b",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Flag a device's behaviour snapshot as botnet-infected vs benign from traffic statistics. N-BaIoT was built to show that per-device behavioural baselines detect Mirai/BASHLITE the instant an attack launches. The deployment question is whether such a model transfers **across devices**, which pooling alone cannot answer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe92593",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Meidan, Bohadana, Mathov, Mirsky, Shabtai, Breitenbacher & Elovici (2018)** \u2014 *N-BaIoT: Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders* (IEEE Pervasive Computing). The paper provides the dataset and a per-device autoencoder detector.\n",
    "- **Antonakakis et al. (2017)** \u2014 *Understanding the Mirai Botnet* (USENIX Security).\n",
    "- **Kolias et al. (2017)** \u2014 DDoS in the IoT: Mirai and other botnets (IEEE Computer).\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",
    "| Meidan et al. (2018) \u2014 per-device deep autoencoders | unsupervised, per-device; instant attack detection but device-specific models |\n",
    "| Supervised classifiers on pooled devices (our setting) | pooling can hide poor cross-device transfer; benign is the minority |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3b486b",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `mkashifn/nbaiot-dataset` (Meidan et al., 2018) |\n",
    "| Rows | **7,062,606** behaviour snapshots (9 devices; label from filename; matches the published UCI count) |\n",
    "| Label | benign vs attack; family = mirai/gafgyt subtype (10 attack families) |\n",
    "| Access | Kaggle API token required |\n",
    "\n",
    "**Honestly:** benign snapshots are a **minority** (**~7.9%**, i.e. the majority-class baseline is 0.9213), so accuracy is inflated by the attack-heavy mix \u2014 per-family recall and the benign false-positive rate are the honest metrics. The device id is deliberately **not** a feature (it lived only in the filename); cross-device transfer, not the pooled split, is the real test.\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 `mkashifn/nbaiot-dataset` -> `/tmp/kg_nbaiot`. It is about **8 GB** 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": "4061d839",
   "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": "271580ae",
   "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": "49185ac6",
   "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": "f0dc4e18",
   "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": "c7c20176",
   "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": "d21a1750",
   "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": "4cb56c05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 7,062,606 behaviour snapshots x 115 features; attack rate 0.9213; families 11\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "# N-BaIoT (Meidan et al., 2018): behaviour-snapshot statistics (damped-window traffic features) of\n",
    "# 9 commercial IoT devices, some infected with the Mirai and BASHLITE(gafgyt) botnets. The class\n",
    "# label lives in the FILENAME ('<device>.benign.csv' vs '<device>.<mirai|gafgyt>.<subtype>.csv').\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "DEST = '/tmp/kg_nbaiot'; os.makedirs(DEST, exist_ok=True)\n",
    "if not glob.glob(DEST + '/**/*.csv', recursive=True):\n",
    "    import kaggle; kaggle.api.authenticate()\n",
    "    print('downloading N-BaIoT (one-time)...')\n",
    "    kaggle.api.dataset_download_files('mkashifn/nbaiot-dataset', path=DEST, unzip=True, quiet=True)\n",
    "# The archive also ships metadata CSVs (device_info / features / data_summary) that are NOT device\n",
    "# traffic \u2014 including them would inflate the row count and create an empty ''-named class. Keep only\n",
    "# the per-device capture files, whose names encode the label: '<device>.benign' / '<device>.<botnet>.<subtype>'.\n",
    "META = ('data_summary', 'device_info', 'features')\n",
    "files = [f for f in sorted(glob.glob(DEST + '/**/*.csv', recursive=True))\n",
    "         if not os.path.basename(f).lower().startswith(META)\n",
    "         and ('benign' in os.path.basename(f).lower() or 'gafgyt' in os.path.basename(f).lower()\n",
    "              or 'mirai' in os.path.basename(f).lower())]\n",
    "assert files, 'N-BaIoT device csvs not found'\n",
    "frames = []\n",
    "for f in files:\n",
    "    name = os.path.basename(f).lower().replace('.csv', '')      # e.g. '1.mirai.udp' or '3.benign'\n",
    "    parts = name.split('.')\n",
    "    fam = 'benign' if 'benign' in parts else '.'.join(parts[1:]) # 'mirai.udp' / 'gafgyt.combo'\n",
    "    d = pd.read_csv(f, low_memory=False)\n",
    "    d['family'] = fam; d['y'] = 0 if fam == 'benign' else 1\n",
    "    frames.append(d)\n",
    "df = pd.concat(frames, ignore_index=True); df.columns = [str(c).strip() for c in df.columns]\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'\n",
    "# We pool all 9 devices. The device id is NOT a feature (it lived only in the filename), so the model\n",
    "# must learn botnet-vs-benign BEHAVIOUR, not which device produced a row \u2014 but per-device transfer\n",
    "# (train on some devices, test on others) is the harder question we flag but do not run.\n",
    "DROP = ['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):,} behaviour snapshots x {len(feat)} features; attack rate {y.mean():.4f}; families {len(set(family))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8185e6f",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5dab995f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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": "07d9ee1e",
   "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": "db3ad7b4",
   "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": "8fac926a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 7,062,606 rows | trained on 120,000 (stratified subsample) | held-out 1,765,652\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.9213  (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": [
       "<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>RandomForest</td>\n",
       "      <td>0.999865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.999852</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.999806</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.999529</td>\n",
       "      <td>0.999799</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.921300</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        RandomForest  0.999865  1.000000      2.2\n",
       "1             XGBoost  0.999852  1.000000      1.2\n",
       "2            LightGBM  0.999806  0.999997      2.2\n",
       "3  LogisticRegression  0.999529  0.999799      0.9\n",
       "4    MajorityBaseline  0.921300  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": "74081498",
   "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": "33efe013",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "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 = 0.0002  (29 benign flagged of 138,983)\n",
      "worst per-family recalls: {'gafgyt.junk': 1.0, 'gafgyt.udp': 1.0, 'gafgyt.tcp': 1.0, 'gafgyt.combo': 1.0, 'gafgyt.scan': 1.0, 'mirai.syn': 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": "cde6640d",
   "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": "81778cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.9665  (feature: HH_jit_L0_01_mean)\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) = 0.648\n",
      "TRAIN/TEST exact-row contamination       = 0.066  (single-feat grade C, contam grade B)\n",
      "==> data trust grade: C   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
      "image/png": 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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": "c7d6573f",
   "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": "57032322",
   "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",
       "    .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>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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (65% rows removed)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (HH_jit_L0_01_mean)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        setting  held_out_auc\n",
       "0                              headline (as-is)           1.0\n",
       "1              de-duplicated (65% rows removed)           1.0\n",
       "2  shortcut feature dropped (HH_jit_L0_01_mean)           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": "60f8b8c9",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0404145b",
   "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": "248da676",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.9213**. 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: `HH_jit_L0_01_mean` at AUC **0.9665**. 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. Overlap is not heavy, but it is **not negligible either** (grade B). The random split still flatters the headline a little. Data-trust grade: **C**. It is the worse of two independent sub-checks. Single-feature AUC 0.9665 scores **C**. Train/test exact-row overlap 0.066 scores **B**. The single-feature check drives the grade, not the overlap check. Train/test overlap separately scores B, so overlap is not the issue here. Operational false-positive rate at threshold 0.5: **0.0002**. Worst per-group recalls, exactly as printed: {`gafgyt.junk`: 1.0, `gafgyt.udp`: 1.0, `gafgyt.tcp`: 1.0, `gafgyt.combo`: 1.0, `gafgyt.scan`: 1.0, `mirai.syn`: 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": "d6ded819",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D. & Elovici, Y. (2018). N-BaIoT: Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders. *IEEE Pervasive Computing*, 17(3), 12\u201322.\n",
    "2. Antonakakis, M. et al. (2017). Understanding the Mirai Botnet. *USENIX Security*.\n",
    "3. Kolias, C. et al. (2017). DDoS in the IoT: Mirai and Other Botnets. *IEEE Computer*.\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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