{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7390cfef",
   "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 UNSW-NB15, a benchmark designed to be more realistic than KDD99.\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",
    "- **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": "e3b1a95c",
   "metadata": {},
   "source": [
    "# Network Intrusion Detection on UNSW-NB15\n",
    "### Model comparison + validity audit on UNSW-NB15 (2.5M records, via Kaggle)\n",
    "\n",
    "**Abstract:** UNSW-NB15 (Moustafa & Slay, 2015) was built to be more realistic and modern than KDD99, with a hybrid of real benign traffic and synthesized attacks. The corpus has ~2.54M records and 9 attack families. We compare four learners and audit the result."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49810ac",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Classify flow records as normal or attack (Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode, Worms). UNSW-NB15 was designed explicitly to fix KDD99's unrealistic distributions \u2014 so it is the right place to ask whether a *more realistic* benchmark still yields shortcut-driven scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a95e47e",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Moustafa & Slay (2015)** \u2014 UNSW-NB15: a comprehensive dataset for NIDS, built to modernize KDD99.\n",
    "- **Moustafa & Slay (2016)** \u2014 statistical analysis and the train/test split.\n",
    "- **Sommer & Paxson (2010)** \u2014 closed-world ML rarely deploys.\n",
    "- **Engelen, Rimmer & Joosen (2021)** \u2014 labeling errors and flow-extractor bugs in the CICIDS2017 dataset. It is cited here as a caution that a benchmark's own labels and features can be defective, not as a finding about UNSW-NB15.\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",
    "| Moustafa & Slay (2016) \u2014 DT/LR/NB | harder than KDD99 by design |\n",
    "| Modern DL on UNSW-NB15 | in-distribution; official split has known overlap |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d18fae",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `mrwellsdavid/unsw-nb15` |\n",
    "| Rows | ~2.54M (UNSW-NB15_1..4.csv combined; headerless) |\n",
    "| Features | 47 flow features (49 cols incl. attack_cat + Label) |\n",
    "| Label | `Label` 0/1; family `attack_cat` |\n",
    "| Access | Kaggle API token required |\n",
    "\n",
    "**Honestly:** IP/port/timestamp columns are dropped as identifiers; `attack_cat` is dropped from features. UNSW-NB15 is *harder* than KDD99, which is the point.\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 `mrwellsdavid/unsw-nb15` -> `/tmp/kg_unsw-nb15`. It is about **605 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": "fa518104",
   "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": "15b7fc09",
   "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": "776fbbb0",
   "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": "648eb1cd",
   "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": "7a6fcd75",
   "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": "d3c84481",
   "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": "ddc1a368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 2,540,047 rows x 41 features; attack rate 0.1265\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "import kaggle; kaggle.api.authenticate()                       # Kaggle token in ~/.kaggle/access_token\n",
    "REF='mrwellsdavid/unsw-nb15'; DEST='/tmp/kg_unsw-nb15'\n",
    "if not os.path.exists(DEST):\n",
    "    kaggle.api.dataset_download_files(REF, path=DEST, unzip=True, quiet=True)\n",
    "NAMES = ['srcip', 'sport', 'dstip', 'dsport', 'proto', 'state', 'dur', 'sbytes', 'dbytes', 'sttl', 'dttl', 'sloss', 'dloss', 'service', 'Sload', 'Dload', 'Spkts', 'Dpkts', 'swin', 'dwin', 'stcpb', 'dtcpb', 'smeansz', 'dmeansz', 'trans_depth', 'res_bdy_len', 'Sjit', 'Djit', 'Stime', 'Ltime', 'Sintpkt', 'Dintpkt', 'tcprtt', 'synack', 'ackdat', 'is_sm_ips_ports', 'ct_state_ttl', 'ct_flw_http_mthd', 'is_ftp_login', 'ct_ftp_cmd', 'ct_srv_src', 'ct_srv_dst', 'ct_dst_ltm', 'ct_src_ltm', 'ct_src_dport_ltm', 'ct_dst_sport_ltm', 'ct_dst_src_ltm', 'attack_cat', 'Label']                                       # the 49 column names (data ships headerless)\n",
    "NROWS = 1_500_000\n",
    "parts = sorted(glob.glob(DEST + '/**/UNSW-NB15_[1-4].csv', recursive=True))  # the 4 raw record files\n",
    "df = pd.concat([pd.read_csv(p, header=None, names=NAMES, low_memory=False, nrows=NROWS) for p in parts],\n",
    "               ignore_index=True)\n",
    "df['y'] = pd.to_numeric(df['Label'], errors='coerce').fillna(0).astype(int)  # Label is 0/1\n",
    "df['family'] = df['attack_cat'].astype(str).str.strip().replace({'': 'Normal', 'nan': 'Normal'})\n",
    "df = df.reset_index(drop=True)\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'\n",
    "DROP = ['Label','attack_cat','y','family','srcip','dstip','sport','dsport','Stime','Ltime']  # ids/time/labels\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",
    "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.loc[:, X.nunique() > 1]; feat = list(X.columns)\n",
    "y = df['y'].to_numpy()\n",
    "print(f'loaded {len(df):,} rows x {len(feat)} features; attack rate {y.mean():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23ffbce0",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6e18afe8",
   "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": "99f477ac",
   "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": "3364f40e",
   "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": "b80a4d40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 2,540,047 rows | trained on 120,000 (stratified subsample) | held-out 635,012\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.8735  (any model must beat THIS, not 0.5, to be interesting)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model: LightGBM\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>LightGBM</td>\n",
       "      <td>0.992687</td>\n",
       "      <td>0.999671</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.992411</td>\n",
       "      <td>0.999657</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.992458</td>\n",
       "      <td>0.999604</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.988137</td>\n",
       "      <td>0.998941</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.873500</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            LightGBM  0.992687  0.999671      1.1\n",
       "1             XGBoost  0.992411  0.999657      0.5\n",
       "2        RandomForest  0.992458  0.999604      0.6\n",
       "3  LogisticRegression  0.988137  0.998941      0.4\n",
       "4    MajorityBaseline  0.873500  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": "6609c824",
   "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": "78981bd1",
   "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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8vf1nz54dp+tRsWLFSG2o+Lw3jz/+uDteq1atImz3XvuhQ4d8P+v1//PPP5HOSX21LFmyhDdr1ixObfmo2r2B7r77brf/pk2bIt338ccfu/sefvhh37aVK1e6bU2aNHFf/T8nrVu3dtv27dt3Rfo7L730UqRzGjJkSNC2nvz9999B24zxaePpcxysjRvTddfnQP2cQHFpr0f13IGvK7DvHtPr0WMaNWrk+lWB1y1nzpzu9zQsLCzCfVu2bHGfP/XN/Hnv/5QpU4L2SaPrA0dHv2fLly8Pv/fee93z6jj6qp8/+uijoH296FSrVi3Kz7eUL1/e3e///1J0vNcWeKtcuXL45s2bo3ycrpP2e/HFF+N0/ggNBAsAJEqw4OzZs+GZMmUKz5EjR9BGaM+ePaMNFqgjE2jEiBHuvg8//DDa5/anzoDuX7FiRdCGlxcA8G6TJ08O2jAKdkubNq0biP3999+jDRZokFKD1ApciBqtavjr3CS6YMH27dvd7UoEC/SeqNOUNWvWCIEYzxNPPOEeO3r06EiN56gCAt7rDOzABBNTQzdYQKdbt27uvieffDLSfaNGjQr63N6gerBGs/d+6rGexo0bu22rVq2KtL8G5nWf9975P0ewTmZ8O00efTbUiQl8Pu99ve+++yI9Rq8zsMOq81JgrWDBgpEa54GOHDnirpcatsGow6jjP/LIIxE6H8WLF4/UCY0rL5CmhvjlBAvUGNdxqlSpEmPgSgE67avgQjB16tRx93/22WfuZx1Pr1ed8WCdBQ1IaMBFwanA8yRYAABILF571WvjDhs2zLUV9Ddf2x966KEIA23+g7PBBpyWLVsWqT01ePDgOJ9XdH8fo/Ldd99FaqMmVBsyNsECUbtJfQH/SUyaRFCkSJHwbNmyRZiAEpvrEWwgOq7vjZ5ffS/1wRRMuBwKNihIce7cuSseLPAmeAQ7R/WbMmbMGGGgWG1QXeuvvvrKPW7WrFm+NpoGnK+77ror0t9ROzIYL1gQrK8QKDGCBYGD5/Fpr8cULIgPL1igvkRUn93p06cHfaz+b9H9XiBB/W39XKJEiaATrrzfo7gEC77++ms3EShv3rzusZrA1LJlSzeRLFiAJbb0efTGAYKpVauWu1+f59jQ5+/LL790AR79v6JgZbt27dwxFAz1D5QGmwT16KOPxvu1IOUiDRGARKH0PMoF6uVKD6Tlna+88kqUjw+2bFdLaSVYTvf40HJcL6+kR+mM/JdfevyXXCq1iZZwaomicszrq/JbeucXSOlKlH9Vyze1VFJLr5UKRilnrmahNb0nWsarJYlakh1IS0S1jFapaAJp2baWhQdbUq6ltnqMUupcjmDvuVf8TkstA2l5umi5arB6Et4S18Dz1Xvu/xq1vFuvLViOWr0mLeUNdk20bNdbIh5XWm47a9YsV9xKS6S1zN4/F69SJV3O74WWF+t4Wurqpd6JivbVUvyoaoh4eVj983QqnZNyoyp9j5Y16zppWXB0KbWC0XJ4/f+glECXw1uaruX5wT6n/vR+e5/3YLRdqaf0nuv6KY2Rlk5ryX5UBcKUHza2eUwBALiavLau/s4rrYnadMr7rrapl3pF1D4N1g7wctzr71xgKiLVAQhsWyvNUKDYpi1RepEJEya4FIvKwx6Ytz4u7aP4tiFjS6kqlapGqUGGDRvmtum8dUylQlRKn7gIvJbxeW+UUlFtSqXMiW0BaaV7UX0D1VdQ2h2lXfGnbQULFrQrSe+zl9IpWL9JqUSVgkb7KQWM0roonZDamkqhqdRDvXv3dm06pWfxT395Of2dYO+B6PgvvPCCS3fTrl07127V8VW3IimI6ryvVHv9cuj9VC2FqD7bSpca7LOt9rf32db5e++XxhC8NEvB+qSxpX29vp/+j9BnQu9tsM9MYlO6t8D/75RGSuf77rvv2sSJE13K1UDea4lLnQmEDoIFABKFl7PUy4keKKrtnmA5Gr16AhrYjC0V61IjQwW8AqmB4OVsVcNYuSZjQ4OR6mQod7+CBs8884xrYGjwNyq9evVyjbVXX33V5VZXfsLAQlxX6z2JqsHvbVejO7bvl66v/7EvR7CGq/eeR3efN5gd3/PV92pMKSdpsOdQHQDVyojqWPGhTocGylWoTvUldCwvZ6VyhiqH5eX8XnjvodcZjk2HTUGDqArlyalTp3zfq0Gqc1e+XuW61U3noY6qGrTKORoTBROVq1R5dWObrzMqcXm9cf098K6POuSBwcWorg8AAElFTHWvvL9zGnyKTrC/c4FtoWATcWIbLNDfXQ0Iq52swU+1k9U+U/tC92mwNqr20ZVsQ8aW6hZo0pDqUHm1FmbPnu3uC5YPPybB2pVxfW/i0h4SXVNNktKgvWovaLKN8qkrsOTV34rqml8Or6jxf//9F6nAsVd/QAECBQz0vQaKvYCMBvtV80Cf62D1Ci6nvxNV216fR9UEUJ9P9Ry82hmqY6UJZSp8nJiiOu8r0V6/XJpYFazAs/fZ1u9PbD7bMY0txLVfpv6dAmoaI9i6davrk+j3QP2SYJ/JuPD+z4mqf+xtj21NiqioHpuCBao9EoxXxP1yXw9SJoIFABKFihfJwYMHg94f1fYrTbM+1NBUY1Kzf640zdwRrSyITsWKFV0RIgUL1EBQETHNlLmavIaLCiMFo8CH/36xeb+8Y13NGSqxEZfz1feaOa4OY2DASEEkzcbwPs/+gjV8Y0MztxQo0KwkFcDzL6qt1QAq5H25vMZnVDPw/HnX4qGHHorVahfRjB51LnVTIEUz8bVKQp1ZFdHSLaYAgIoFa+aXCpslxuuN7e+B97VNmzau4BoAACmJ93fugw8+cMVb4yKwLeQ/ESeutOJYgYJgBVQ1C1kD20mJBuC6du3qBmRXr15t5cuXd+069Q3iUxQ4WLsyru9NXNpDauPqOmuQVTP0AwfXvZnfCcFbmasB42CrC7zVnwoKKAij9rEXENB9b731lgtkqH+n66YCzVeivxNd214z8j/66CMXPPnuu+/c4LImgqmAdN68eS97lezliOq8r0R7PaHOzbv+eh+DrTyIav+Y+nixpd9XFSNXUEpFgNXGV/9MK4K0gkQBIAXQYjuZ0J+CSOrvaXVE4Mom/d7p/zn1/xTIuRz63EngCqzAgEx8V8IjZYt+LT4AJBClz1EjevPmzXby5MlI96uxcqWoERnVagM14vXHWLNAEiJNiJf6xT+FTHSrCw4fPmznzp1z3ycEb1lmsOuhhotmC6lRFmw2jYIqctNNNwV9v4K9xk8//dR9vfHGGy0p2bdvn5vdFpvz1fd6bcFmZWibrmWwaxLf90GpqESdPv9AgRd08maBXA7NgNLvhc4/qgZk4L6aMRUfaoBqwH/RokWuA6cUW5qhExM1yNVBCUxpEB8KxHkBiJh+F7333vssxPR7oP/L1PlWqqPLmYEIAEBS5P0NjW874Erx2keaUBMoLulFrialG9JgqFYXa0KQ2n3xWVVwpd4br82i/lewVdX+NBlG/QGl/AkMFGg2t5e2MSF4g8NKmxSMVphooo6CARrMVZ9Sg/XiBQ2U8unLL790x9Is8SvR34kNtV11zcaMGWNTp071BXNi0wdITDG113XeV/Oc4/rZ9trv6pMGO8+o2vXRUf9HQR6tvFAQYuHChe7a6Bq1aNHC/V5o9r7+/4lNXz8w2KWAUiD1zTRZSp+hyw3UeGlYowo6eL9fVapUuaznQcpEsABAolBKF6Va0Sz6wDzfary98cYbV+y5NENfMwOCUS7JJ554wg3QN2/e3L766qug+wVrTMZEM0tmzJjhvg+W7z7YcmUNkKpBGZv9vT/yUTWkg/FWK2iwPNh7otyVCt48+eSTEe5Tg1ENXs2euP/++yM9VilYvNfq0etQ40lLWJX/NilRI/LRRx+N0LDTLA69Rg3Qe3l6xVtx8vjjj7vGm0ffa1m5KLdvXET3PqjWQbBGrWb8KLXVlaCZJvq8afbUww8/HKmBq06gtwRWnQd9LjQD5qmnngraANfnQ9fP+9yrcxZIA+laoSHqpEVHs2o0O0sdvmA1TeJKs3bU6P7xxx9t3LhxQWfWaKm7t9pIHUl1NhRE9Kef1Wm5/vrrXU5U0edl4MCB7lo++OCDQYM5uk+1JwAASG6UDlHt5RdffNENwAajWeb+baSEEFX7SCloxo4da0mR6hmpLaM2jfL+a6Be7a/Eem804KtaCmqraJAzMIWQ+kOauOS1/9Re0yx5/xRTas8NGjQoQfOce/0gb7AzkF6H6kYpgKRZ8GqTeQOrqkmmz4pWmuh1B9agupz+TlTUfwzW/vNmufu3e6PrA1xNcW2v67z12Yhq0pJWoURV3yw+unXr5n5flLYs2Ap99V38/y8oUqSIm+mv/ojqAAbrk15ubQXVddCx1K7X77NqJSi1mD6vStGllRixoVoCCmBpFYf6Vx71RTQu4QUa/emzrD5/4OdGgb9gk5W0ffjw4e57/36tP/1+eb9LQCDSEAG4bJqdHxUNIEc1MKi8iJoNorQqGzZscIN5+uOraL1mEysXZkzFSGNDjXT9MW7VqpWbJaIGoP4oen8YR4wY4ZZEayBUA4UaWNRsauVBVZBAM9C1zFWi+mOqxorXONKx9Dq01FhFzBTN9/5YR0fXScsa46Js2bK+54wNzbrR8yjvvQZIvfyNGuzU8k29JxoMVSNL+em1bFedAb0nalRre7DCwM2aNXN5WfWatbRajXct11TDSoXdrsT7eCVplpE+c3qvmzZt6t5nvUZ91efRvyCZlg+rYaj7tSRV75GXq1UNUgW91OmI62dSBfq0gkQz5DQgrgbxgAED3GwpfQ51/fQ7oQ6QOhu6thrEjm1BupjovdSMITV29flV8V91oPSaNAN/6dKlvs6a9lVASL8rysOqc1JOUM1K04ocfVa05FufDXUidL+CRLq+Kgquxu+aNWvcvlox4X1uo6LzUUdFqX2ulHnz5rnXo5y2yt/ppULQ61J6ADXA1bnUe/v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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 = 0.0037  (2,065 benign flagged of 554,691)\n",
      "worst per-family recalls: {'Fuzzers': 0.638, 'Analysis': 0.858, 'Shellcode': 0.943, 'Exploits': 0.983, 'DoS': 0.989, 'Backdoors': 0.993}\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": "97150469",
   "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": "225aa6ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.9918  (feature: ct_state_ttl)\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) = 0.206\n",
      "TRAIN/TEST exact-row contamination       = 0.133  (single-feat grade D, contam grade B)\n",
      "==> data trust grade: D   (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": "bab95841",
   "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": "794c2c43",
   "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>0.999671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (21% rows removed)</td>\n",
       "      <td>0.998731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (ct_state_ttl)</td>\n",
       "      <td>0.999659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   setting  held_out_auc\n",
       "0                         headline (as-is)      0.999671\n",
       "1         de-duplicated (21% rows removed)      0.998731\n",
       "2  shortcut feature dropped (ct_state_ttl)      0.999659"
      ]
     },
     "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": "3bed35ad",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a2d8fa1",
   "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": [
      "LightGBM 3-fold CV ROC-AUC = 0.9995 +/- 0.0001  (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": "4bbb5945",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "Even on UNSW-NB15 \u2014 designed to be more realistic than KDD99 \u2014 the learners score high. The ledger below reports the strongest single feature, the duplicate and overlap rates, and what the score does when each is removed. Read it before attributing the result to any one cause. The lesson: a *newer* benchmark is not automatically a *harder* one for the model; per-family recall and cross-distribution tests remain the honest evidence.\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.8735**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **LightGBM** (3-fold CV ROC-AUC **0.9995**). Strongest *single* feature: `ct_state_ttl` at AUC **0.9918**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.999671 \u2192 0.999659**. 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 **0.998731**. Repeated rows account for a negligible part of the headline. Data-trust grade: **D**. It is the worse of two independent sub-checks. Single-feature AUC 0.9918 scores **D**. Train/test exact-row overlap 0.133 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. 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: **0.0037**. Worst per-group recalls, exactly as printed: {`Fuzzers`: 0.638, `Analysis`: 0.858, `Shellcode`: 0.943, `Exploits`: 0.983, `DoS`: 0.989, `Backdoors`: 0.993}. The weakest group sits at **0.638**, which is where detection is thinnest. **Disclosed limitation:** categorical columns are integer-encoded before the split. The encoder therefore sees the test set's category values. On an all-numeric corpus that step is a no-op. The mapping never consults the label, so no *label* information leaks. It is still transductive. A deployed system would need an unseen-category bucket. **How the audit numbers are computed:** overlap is measured on the first 50,000 held-out rows, so read it as a sampled estimate. Each ablation re-splits and refits, so tiny differences are re-split noise. The de-duplication variant keeps the first label when a feature vector appears twice. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defc31d1",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Moustafa, N. & Slay, J. (2015). UNSW-NB15: a comprehensive data set for network intrusion detection systems. *MilCIS*.\n",
    "2. Moustafa, N. & Slay, J. (2016). The evaluation of Network Anomaly Detection Systems: statistical analysis of the UNSW-NB15 data set. *Info. Security J.*\n",
    "3. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*.\n",
    "4. Engelen, G., Rimmer, V. & Joosen, W. (2021). Troubleshooting an Intrusion Detection Dataset: the CICIDS2017 Case Study. *IEEE S&P Workshops*."
   ]
  }
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