{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4cbf9f50",
   "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 the CSE-CIC-IDS2018 web-attack day, where the attack class is a fraction of a percent.\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. Explain why a 0.9995 accuracy can mean the model found nothing.\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": "54b20633",
   "metadata": {},
   "source": [
    "# CIC-IDS2018 Case Study \u2014 Web Attacks (Brute Force / XSS / SQLi)\n",
    "### Model comparison + validity audit on the web attacks (brute force / xss / sqli) day of CSE-CIC-IDS2018\n",
    "\n",
    "**Abstract:** We study one day of the CSE-CIC-IDS2018 flow corpus. Its attack traffic is the day's **web attacks**, which carry three labels \u2014 `Brute Force -Web`, `Brute Force -XSS` and `SQL Injection`. All three are reported separately in the per-family recall breakdown in section 9. Benign flows still dominate the day numerically. The attack is the minority class, and the loader prints the exact rate. We load \u22651,000,000 real CICFlowMeter records. The four learners then fit a **120,000-row stratified subsample**, so every score below is a subsample number. The question is whether the near-perfect in-distribution scores reflect detection, or a defect in the features. Engelen et al. (2021) found bugs in CICFlowMeter itself. That tool built this day's features too, so their tool-level findings are a live suspicion here. Their label corrections are specific to CICIDS2017 and do not carry over to this 2018 capture."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7de136e",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Detect Brute-Force-Web, XSS, and SQL-Injection among benign traffic on the 2018-02-23 capture. Web attacks are **extremely rare here (0.05% of flows, i.e. a 0.9995 majority baseline)**, making this a severe class-imbalance problem where aggregate accuracy is meaningless \u2014 the interesting question is recall on the rare attack families."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c1b672",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Sharafaldin, Lashkari & Ghorbani (2018)** \u2014 the **CIC-IDS2017** dataset paper (*ICISSP 2018*, pp. 108\u2013116): realistic profiled benign traffic plus a labeled attack schedule, with 80 CICFlowMeter flow features. It documents the **2017** capture, not the CSE-CIC-IDS2018 corpus studied here. The CIC\u2019s 2018 dataset page lists no dataset paper of its own for the 2018 capture. It offers this one as the write-up of a *similar* dataset and its generation principles.\n",
    "- **Engelen, Rimmer & Joosen (2021)** \u2014 *Troubleshooting an Intrusion Detection Dataset: the CICIDS2017 Case Study* \u2014 found labeling errors and CICFlowMeter feature bugs that inflate scores.\n",
    "- **Rosay, Cheval, Carlier & Leroux (2022)** \u2014 *Network Intrusion Detection: A Comprehensive Analysis of CIC-IDS2017* (ICISSP): documents CICFlowMeter implementation flaws affecting these flow features.\n",
    "- **Sommer & Paxson (2010)** \u2014 closed-world ML scores rarely survive deployment.\n",
    "- **Apruzzese et al. (2023)** \u2014 *The Role of Machine Learning in Cybersecurity* (ACM DTRAP): a survey of where ML is and is not actually deployed in security practice. Cited for that framing, not as a study of dataset shortcuts.\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",
    "| Sharafaldin et al. (2018) \u2014 RF among seven learners on CIC-IDS**2017** (not the 2018 day studied here) | in-distribution; CICFlowMeter features later shown buggy |\n",
    "| Engelen et al. (2021) \u2014 re-labeled CICIDS2017 | original labels/features partly wrong |\n",
    "| Typical DL-NIDS papers | benign-majority base rate inflates accuracy; per-family recall varies |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94a6d07",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | CSE-CIC-IDS2018, AWS Open Data `s3://cse-cic-ids2018/` (no credentials) |\n",
    "| File | `Friday-23-02-2018_TrafficForML_CICFlowMeter.csv` |\n",
    "| Rows | \u2265 1,000,000 flow records (bounded S3 slice) |\n",
    "| Features | 80 CICFlowMeter columns in the file \u2192 **68 used** after dropping label/ID and constant columns (the loader prints the exact count) |\n",
    "| Label | `Benign` vs the day's attack families |\n",
    "\n",
    "**Honestly:** CICFlowMeter's feature implementation has documented bugs (Engelen et al. 2021; Rosay et al. 2022), and accuracy is inflated by the benign-majority base rate. We report per-attack-family recall and audit single-feature shortcuts for this reason.\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 the public AWS Open Data bucket `s3://cse-cic-ids2018/`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb05553",
   "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": "39c05638",
   "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": "3faa3ad2",
   "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": "188abcd3",
   "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": "f0ae4b73",
   "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": "fa37ee4a",
   "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": "a4f451d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 1,048,575 flows x 68 features; attack rate 0.0005\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "FILE = 'Friday-23-02-2018_TrafficForML_CICFlowMeter.csv'; SHORT = 'webattacks'\n",
    "PREFIX = 's3://cse-cic-ids2018/Processed Traffic Data for ML Algorithms/'\n",
    "CACHE = f'/tmp/cic_{SHORT}.csv'\n",
    "if not os.path.exists(CACHE):                                 # bounded S3 download, no credentials\n",
    "    os.system(f'aws s3 cp \"{PREFIX}{FILE}\" - --no-sign-request 2>/dev/null | head -n 1200000 > \"{CACHE}\"')\n",
    "df = pd.read_csv(CACHE, low_memory=False)                     # parse the day's flow records\n",
    "df = df[pd.to_numeric(df['Dst Port'], errors='coerce').notna()].reset_index(drop=True)  # drop repeated-header junk\n",
    "df['Label'] = df['Label'].astype(str).str.strip()            # clean labels\n",
    "df['y'] = (df['Label'] != 'Benign').astype(int)              # 1 = attack, 0 = benign\n",
    "df['family'] = df['Label']                                    # attack type doubles as family\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'   # honesty gate: >= 1M rows\n",
    "DROP = ['Label','Timestamp','y','family']\n",
    "feat = [c for c in df.columns if c not in DROP]\n",
    "X = df[feat].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 constants; align feat\n",
    "y = df['y'].to_numpy()                                        # STANDARD CONTRACT: binary label\n",
    "print(f'loaded {len(df):,} flows x {len(feat)} features; attack rate {y.mean():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a23a7e",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3100298d",
   "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": "deda1099",
   "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": "e3354727",
   "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": "150b5210",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 1,048,575 rows | trained on 120,000 (stratified subsample) | held-out 262,144\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.9995  (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>0.999710</td>\n",
       "      <td>0.996349</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.999565</td>\n",
       "      <td>0.972244</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.999729</td>\n",
       "      <td>0.964414</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.996037</td>\n",
       "      <td>0.514258</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.999500</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  0.999710  0.996349      0.6\n",
       "1  LogisticRegression  0.999565  0.972244      0.2\n",
       "2        RandomForest  0.999729  0.964414      0.7\n",
       "3            LightGBM  0.996037  0.514258      1.2\n",
       "4    MajorityBaseline  0.999500  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": "75a84e28",
   "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": "43572e3a",
   "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 = 0.0001  (19 benign flagged of 262,002)\n",
      "worst per-family recalls: {'Brute Force -Web': 0.536, 'SQL Injection': 0.556, 'Brute Force -XSS': 0.852}\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": "bf18af4d",
   "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": "f1c3b6e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.7457  (feature: Bwd Pkts/s)\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) = 0.132\n",
      "TRAIN/TEST exact-row contamination       = 0.088  (single-feat grade A, contam grade B)\n",
      "==> data trust grade: B   (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": "f373e7f4",
   "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": "b85c927c",
   "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>0.996349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (13% rows removed)</td>\n",
       "      <td>0.999049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (Bwd Pkts/s)</td>\n",
       "      <td>0.994340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 setting  held_out_auc\n",
       "0                       headline (as-is)      0.996349\n",
       "1       de-duplicated (13% rows removed)      0.999049\n",
       "2  shortcut feature dropped (Bwd Pkts/s)      0.994340"
      ]
     },
     "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": "3ba4f05b",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d312fb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost 3-fold CV ROC-AUC = 0.9746 +/- 0.0352  (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": "fe27b1ba",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "With attacks at just **0.05%** of flows (majority baseline accuracy 0.9995), a model can reach >0.99 accuracy by predicting 'benign' almost always. That is why we report per-family recall and PR curves, not accuracy. The honest metric is whether the rare web-attack families are recalled at all. The accuracy figure is flattered by the benign base rate, though the model here is *not* useless. See the held-out AUC and the per-family recalls in the ledger below. This is the imbalance lesson every NIDS practitioner must internalize (Sommer & Paxson, 2010).\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.9995**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **XGBoost** (3-fold CV ROC-AUC **0.9746**). Strongest *single* feature: `Bwd Pkts/s` at AUC **0.7457**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.996349 \u2192 0.994340**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication **raises** the AUC, to **0.999049**. That is not evidence the headline is safe. Collapsing duplicates removes the hardest rows. Identical feature vectors carrying conflicting labels get resolved to one label. The de-duplicated task is therefore *easier*, not cleaner. Data-trust grade: **B**. It is the worse of two independent sub-checks. Single-feature AUC 0.7457 scores **A**. Train/test exact-row overlap 0.088 scores **B**. The overlap check drives the grade, not the single-feature check. That says the split leaks, not that features are clean; the single-feature check separately scores A. Operational false-positive rate at threshold 0.5: **0.0001**. Worst per-group recalls, exactly as printed: {`Brute Force -Web`: 0.536, `SQL Injection`: 0.556, `Brute Force -XSS`: 0.852}. The weakest group sits at **0.536**, which is where detection is thinnest. **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": "21a0a788",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Sharafaldin, I., Lashkari, A.H. & Ghorbani, A.A. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. *ICISSP*.\n",
    "2. Engelen, G., Rimmer, V. & Joosen, W. (2021). Troubleshooting an Intrusion Detection Dataset: the CICIDS2017 Case Study. *IEEE S&P Workshops*.\n",
    "3. Rosay, A., Cheval, E., Carlier, F. & Leroux, P. (2022). Network Intrusion Detection: A Comprehensive Analysis of CIC-IDS2017. *8th Int. Conf. on Information Systems Security and Privacy (ICISSP)*, 25\u201336.\n",
    "4. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*.\n",
    "5. Apruzzese, G. et al. (2023). The role of machine learning in cybersecurity. *ACM DTRAP*."
   ]
  }
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