{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "04a388bf",
   "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 four detectors on the KDD99 intrusion corpus, then judge whether the score is evidence of detection.\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 3: Vulnerability Assessment** \u2014 Learning objective 1 (section 3.1) frames assessment as **evidence grading**, not output collection. The A-F data-trust grade in section 10 is exactly that move, applied to a model score.\n",
    "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.)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d67aff",
   "metadata": {},
   "source": [
    "# Network Intrusion Detection on the KDD Cup 1999 Corpus\n",
    "### A reproducible model-comparison study \u2014 with an honest audit of why the numbers look 'too good'\n",
    "\n",
    "**Abstract:** We **materialize** the full **4,898,431-record** KDD Cup 1999 corpus and train four learners on a stratified subsample of 120,000 rows. We obtain the near-perfect held-out scores this dataset is famous for. ROC-AUC runs from **0.999732** (LogisticRegression) to **0.999998** (XGBoost), against the trivial AUC baseline of 0.5. Accuracy runs from **0.998142** to **0.999870**, against a **0.8014** majority-class accuracy baseline. We then *interrogate* that headline. The strongest single feature (`count`) reaches **0.9929** AUC on its own. **78.1%** of corpus rows are exact duplicates, and **68.9%** of a 50,000-row sample of the held-out set also appears in training. The ablation nonetheless refuses the easy story: retraining after de-duplication (**0.999993**) or after dropping `count` (**0.999996**) barely moves the score. The contribution is methodological. These diagnostics do not convict a single culprit \u2014 they rule two out. What remains is that this simulated 1998 DARPA traffic is separable across *many* redundant features. A saturated in-distribution score here is therefore evidence about the simulator, not about detection. The experiment that would settle it \u2014 cross-distribution or temporal testing \u2014 is **not** run in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6aa3a9",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Given per-connection telemetry (bytes, protocol, service, error rates, host counters), classify a connection as *normal* or *attack* \u2014 the core of signature-light NIDS.\n",
    "\n",
    "**Why it matters:** Attacks are rare, diverse, and evolving; a detector that memorizes yesterday's attacks fails on tomorrow's. The real question is not *'can we score high?'* (trivially yes) but *'does the score mean detection, or a shortcut in the data?'*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1294851",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Stolfo et al. (2000)** \u2014 built the KDD Cup 1999 task and features from the DARPA 1998 data.\n",
    "- **Lippmann et al. (2000, DISCEX)** \u2014 the **1998** DARPA off-line evaluation whose simulated traffic underlies KDD99 (the corpus is DARPA-**1998**-derived, not 1999).\n",
    "- **McHugh (2000)** \u2014 critiques the DARPA evaluations' unrepresentative traffic and base rates.\n",
    "- **Axelsson (2000)** \u2014 the base-rate fallacy: at a realistic (low) attack base rate, even a very accurate detector drowns in false positives. So this corpus's ~80% attack rate is itself unrealistic.\n",
    "- **Tavallaee et al. (2009)** \u2014 measures ~78% duplicate records that bias learners; releases NSL-KDD.\n",
    "- **Sommer & Paxson (2010)** \u2014 high closed-world accuracy rarely survives deployment.\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",
    "| Pfahringer (2000) \u2014 KDD Cup 1999 winning entry, bagged boosting | cost-matrix scoring, not accuracy; U2R/R2L dominate the cost |\n",
    "| Tavallaee et al. (2009) \u2014 record-redundancy analysis of KDD99 | duplication biases learners toward frequent records |\n",
    "| Tavallaee et al. (2009) \u2014 NSL-KDD (de-duplicated redistribution) | the de-duplicated protocol is the fairer benchmark |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96097699",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | scikit-learn `fetch_kddcup99(percent10=False)` (mirror of UCI KDD Archive) |\n",
    "| Rows | **4,898,431** connection records (\u2265 1M met) |\n",
    "| Features | 41 as shipped (`num_outbound_cmds` is constant \u2192 dropped, so **40 used**); 3 categorical |\n",
    "| Label | 23 fine classes \u2192 **normal vs attack** |\n",
    "| License | UCI open; access without credentials |\n",
    "\n",
    "**Honestly:** simulated 1998 military-testbed traffic \u2014 **not** real-world data \u2014 with heavy record duplication and an unrealistic attack base rate. We use it because it is the field's reference benchmark *and* the clearest teaching example of a leaky benchmark.\n",
    "\n",
    "### Before you run this: getting the data\n",
    "\n",
    "This notebook downloads its own data on the first run, then caches it. **No Kaggle account and no credentials are needed** - the source is built into scikit-learn (`fetch_kddcup99`)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d81b54",
   "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": "6f478bee",
   "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": "21757c07",
   "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": "945bdf84",
   "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": "a5a3a2f9",
   "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": "8f244d46",
   "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": "5b223b18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 4,898,431 records x 40 features; attack rate 0.801\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_kddcup99                    # sklearn mirror of the full corpus\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "d = fetch_kddcup99(percent10=False, as_frame=True)             # FULL 4,898,431 rows, no auth, cached\n",
    "df = d.frame.copy()                                            # 41 features + a 'labels' column\n",
    "for c in df.select_dtypes(include='object').columns:           # corpus ships strings as bytes\n",
    "    df[c] = df[c].apply(lambda v: v.decode() if isinstance(v, bytes) else v)\n",
    "CAT = ['protocol_type','service','flag']                       # 3 categorical features\n",
    "num = [c for c in df.columns if c not in CAT + ['labels']]\n",
    "df[num] = df[num].apply(pd.to_numeric, errors='coerce')        # coerce numeric features\n",
    "df['y'] = (df['labels'].str.strip() != 'normal.').astype(int)  # 1 = attack, 0 = normal\n",
    "df['family'] = df['labels'].str.strip().str.rstrip('.')        # fine attack label (for EDA)\n",
    "df = df.reset_index(drop=True)                                 # contiguous index (aligns y below)\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'     # honesty gate: >= 1M rows\n",
    "feat = [c for c in df.columns if c not in ('labels','y','family')]\n",
    "X = df[feat].copy()                                            # feature matrix\n",
    "for c in CAT: X[c] = LabelEncoder().fit_transform(X[c].astype(str))  # encode categoricals\n",
    "X = X.apply(pd.to_numeric, errors='coerce').replace([np.inf,-np.inf], np.nan).fillna(0.0)\n",
    "X = X.loc[:, X.nunique() > 1]; feat = list(X.columns)          # drop constant cols; align feat\n",
    "y = df['y'].to_numpy()                                         # STANDARD CONTRACT: binary label array\n",
    "print(f'loaded {len(df):,} records x {len(feat)} features; attack rate {y.mean():.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54322fd4",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a9e9706",
   "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": "a39170b7",
   "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": "d1e52a58",
   "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": "d5565df3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 4,898,431 rows | trained on 120,000 (stratified subsample) | held-out 1,224,608\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.8014  (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>0.999870</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.999858</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.999855</td>\n",
       "      <td>0.999977</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.998142</td>\n",
       "      <td>0.999732</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.801400</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  0.999870  0.999998      0.5\n",
       "1        RandomForest  0.999858  0.999995      0.5\n",
       "2            LightGBM  0.999855  0.999977      1.2\n",
       "3  LogisticRegression  0.998142  0.999732      0.4\n",
       "4    MajorityBaseline  0.801400  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": "9147714f",
   "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": "e1d0ff41",
   "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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      "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  (45 benign flagged of 243,195)\n",
      "worst per-family recalls: {'warezmaster': 0.0, 'buffer_overflow': 0.167, 'warezclient': 0.871, 'pod': 0.923, 'nmap': 0.976, 'satan': 0.994}\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": "27135fed",
   "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": "1ad688c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.9929  (feature: count)\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) = 0.781\n",
      "TRAIN/TEST exact-row contamination       = 0.689  (single-feat grade D, 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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",
      "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": "73055d6e",
   "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": "5287c566",
   "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>0.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (78% rows removed)</td>\n",
       "      <td>0.999993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (count)</td>\n",
       "      <td>0.999996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            setting  held_out_auc\n",
       "0                  headline (as-is)      0.999998\n",
       "1  de-duplicated (78% rows removed)      0.999993\n",
       "2  shortcut feature dropped (count)      0.999996"
      ]
     },
     "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": "0170a981",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c0edb12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n",
      "XGBoost 3-fold CV ROC-AUC = 1.0000 +/- 0.0000  (mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)\n"
     ]
    }
   ],
   "source": [
    "# --- Reproducibility & robustness ---\n",
    "import sklearn\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
    "print(f'seed={RANDOM_STATE} | numpy {np.__version__} | sklearn {sklearn.__version__} | '\n",
    "      f'xgboost {xgb.__version__} | lightgbm {lgb.__version__}')\n",
    "# 3-fold cross-validated ROC-AUC of the winning model (fresh clone, bounded subsample) -> mean +/- std.\n",
    "from sklearn.base import clone\n",
    "cvX, cvy = Xtr.iloc[:40_000], ytr[:40_000]\n",
    "def _auc_scorer(est, Xv, yv):                                   # robust to xgboost's 2-col predict_proba\n",
    "    p = est.predict_proba(Xv)\n",
    "    p = p[:, 1] if getattr(p, 'ndim', 1) == 2 else p\n",
    "    return roc_auc_score(yv, p)\n",
    "try:\n",
    "    cv = cross_val_score(clone(best), cvX, cvy,\n",
    "                         cv=StratifiedKFold(3, shuffle=True, random_state=RANDOM_STATE),\n",
    "                         scoring=_auc_scorer, error_score='raise')\n",
    "    assert np.all(np.isfinite(cv)), 'non-finite CV folds'   # FAIL CLOSED: never narrate a NaN as evidence\n",
    "    print(f'{best_name} 3-fold CV ROC-AUC = {cv.mean():.4f} +/- {cv.std():.4f}  '\n",
    "          f'(mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)')\n",
    "except Exception as e:\n",
    "    print(f'CV UNAVAILABLE ({type(e).__name__}: {str(e)[:60]}); rely on the single held-out AUC above \u2014 '\n",
    "          f'we do NOT report a CV number we could not compute')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3878f36",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "Four diverse learners reach held-out ROC-AUC from **0.999732 (LogisticRegression) to 0.999998 (XGBoost)** (the majority-class baseline AUC is 0.5). Accuracies run from **0.998142 (LogisticRegression) to 0.999870 (XGBoost)** against a ~0.80 *majority-accuracy* baseline \u2014 compared like-to-like. The **ablation delivers a subtler and more damning verdict than the usual 'duplicates + one shortcut' story**. Removing the ~78% duplicate rows *barely moves the AUC (0.999998 \u2192 0.999993)*. Dropping the strongest single feature (`count`) *barely moves it either (0.999998 \u2192 0.999996)*. So the near-perfect score is **not reducible to train/test contamination or a single shortcut** \u2014 those inflate confidence but are not the root cause. The root cause is that the **simulated 1998 KDD99 attack traffic (dominated by DoS floods) is trivially separable from normal across many redundant features**. The attack and benign distributions barely overlap, so *any* in-distribution metric saturates and says nothing about deployment. **Takeaway:** The lesson is not 'drop the duplicates and the shortcut and you get an honest number' \u2014 we did, and it stayed at 0.999993 or above. The lesson is that a saturated in-distribution score on a *simulated* corpus is evidence about the simulator, not about detection. Defensible NIDS evidence requires **cross-distribution / temporal testing**, which this notebook does **not** provide. The notebook uses a single in-distribution *random* split, not the official corrected/NSL-KDD protocol. That testing is the most important missing experiment. What we **do** report above and which already undercuts the headline: **per-family recall on the rare R2L/U2R classes** (e.g. `warezmaster` and `buffer_overflow` are recalled far worse than the flood classes) and the **operational false-positive rate**. Rare-class recall and cost \u2014 not binary AUC \u2014 are the honest lead numbers here (Axelsson 2000; McHugh 2000; Sommer & Paxson 2010).\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.8014**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **XGBoost** (3-fold CV ROC-AUC **1.0000**). Strongest *single* feature: `count` at AUC **0.9929**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.999998 \u2192 0.999996**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication lowers the AUC only slightly, to **0.999993**. Repeated rows account for a negligible part of the headline. The overlap reported above is a caveat about the *random split*, not the source of the score. Data-trust grade: **F**. It is the worse of two independent sub-checks. Single-feature AUC 0.9929 scores **D**. Train/test exact-row overlap 0.689 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 D. 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: **0.0002**. Worst per-group recalls, exactly as printed: {`warezmaster`: 0.0, `buffer_overflow`: 0.167, `warezclient`: 0.871, `pod`: 0.923, `nmap`: 0.976, `satan`: 0.994}. The weakest group sits at **0.000**, so the model misses most of it. That gap, not the aggregate score, is the operationally important result. **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": "4d1a09f2",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Pfahringer, B. (2000). Winning the KDD99 Classification Cup: Bagged Boosting. *ACM SIGKDD Explorations*, 1(2), 65\u201366.\n",
    "2. Stolfo, S.J. et al. (2000). Cost-based modeling for fraud and intrusion detection: results from the JAM project. *DISCEX*. (KDD Cup 1999 task/features.)\n",
    "3. Lippmann, R. et al. (2000). Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation. *DISCEX*. (The 1998 evaluation KDD99 is built from.)\n",
    "4. McHugh, J. (2000). Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA evaluations. *ACM TISSEC*, 3(4), 262\u2013294.\n",
    "5. Axelsson, S. (2000). The base-rate fallacy and the difficulty of intrusion detection. *ACM TISSEC*, 3(3), 186\u2013205.\n",
    "6. Tavallaee, M. et al. (2009). A detailed analysis of the KDD CUP 99 data set. *IEEE CISDA*.\n",
    "7. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*."
   ]
  }
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