{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6178a7f5",
   "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 brute-force day, then grade how much of the score is trustworthy.\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": "1cf6f93b",
   "metadata": {},
   "source": [
    "# CIC-IDS2018 Case Study \u2014 FTP/SSH Brute Force\n",
    "### Model comparison + validity audit on the ftp/ssh brute force day of CSE-CIC-IDS2018\n",
    "\n",
    "**Abstract:** We study one day of the CSE-CIC-IDS2018 flow corpus. Its attack traffic is **ftp/ssh brute force**. 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": "aa6415a5",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Detect FTP-BruteForce and SSH-BruteForce connections among benign traffic on the 2018-02-14 capture. Brute force is high-volume and repetitive, so we expect it to be *easy* \u2014 and easy is exactly what makes the leakage question sharp."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8bdb1f7",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Sharafaldin, Lashkari & Ghorbani (2018)** \u2014 the CIC-IDS2017/2018 dataset paper: realistic profiled benign traffic plus a labeled attack schedule. CICFlowMeter extracts more than 80 network-traffic features. The per-day CSV used here has 80 columns, one of which is the label.\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. It is 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 on CIC-IDS2017/18 | 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": "f29de435",
   "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 | `Wednesday-14-02-2018_TrafficForML_CICFlowMeter.csv` |\n",
    "| Rows | 1,048,575 flow records, exactly as the loader prints (the S3 download is bounded at 1.2M lines) |\n",
    "| Features | 80 columns in this CSV: 79 CICFlowMeter flow features plus `Label`. The loader drops `Label` and `Timestamp`, then drops every column with a single distinct value, and the **68** it prints is what remains. This file carries no flow-ID or IP column, so there is no identifier column to drop. |\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. One more caveat belongs here rather than in a footnote. `Dst Port` is kept as a model feature, and this day's attacks are FTP brute force (port 21) and SSH brute force (port 22). So on this capture that column is closer to a name for the attack class than to a description of behaviour. Section 10 says what that does to the score.\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": "a4858333",
   "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": "98555d6c",
   "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": "209c81f5",
   "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": "2b0d9531",
   "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.\"/>\n",
    "\n",
    "*Reading stage 2 of the figure:* the \"encode categoricals\" step in that box is template text for this notebook series and **does not run on this corpus**. Nothing categorical is encoded here and no encoder is fitted. Every column the loader keeps is already numeric. So the loader coerces each one with `pd.to_numeric`, maps non-finite values to 0.0, and then drops the columns with a single distinct value. The rest of the stage \u2014 relabelling, dropping the label and timestamp columns, dropping constants \u2014 is what the loader in section 6 does."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be1f7a6b",
   "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": "279de9b5",
   "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": "f96c212e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 1,048,575 flows x 68 features; attack rate 0.3633\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "FILE = 'Wednesday-14-02-2018_TrafficForML_CICFlowMeter.csv'; SHORT = 'bruteforce'\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": "1d9346db",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbe7745e",
   "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": "b68e49d9",
   "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": "53fc8c2d",
   "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": "b03c1462",
   "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 = 0.6367  (any model must beat THIS, not 0.5, to be interesting)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model: RandomForest\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>train_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.999985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.999969</td>\n",
       "      <td>0.999991</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.999874</td>\n",
       "      <td>0.999983</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.636700</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy   roc_auc  train_s\n",
       "0        RandomForest  0.999992  1.000000      0.5\n",
       "1             XGBoost  0.999985  1.000000      0.3\n",
       "2            LightGBM  0.999969  0.999991      0.6\n",
       "3  LogisticRegression  0.999874  0.999983      0.2\n",
       "4    MajorityBaseline  0.636700  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": "0a6fb795",
   "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": "0dec066c",
   "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 = 0.0000  (1 benign flagged of 166,907)\n",
      "worst per-family recalls: {'SSH-Bruteforce': 1.0, 'FTP-BruteForce': 1.0}\n"
     ]
    }
   ],
   "source": [
    "# --- Results for the best model: confusion, ROC, PR, importances, per-family recall ---\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, precision_recall_curve, recall_score\n",
    "pb = best.predict_proba(Xte)[:, 1]; pred = (pb > 0.5).astype(int)\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "# (1) confusion matrix\n",
    "cm = confusion_matrix(yte, pred); ax[0].imshow(cm, cmap='Blues')\n",
    "ax[0].set_title(f'{best_name}: confusion'); ax[0].set_xticks([0,1]); ax[0].set_yticks([0,1])\n",
    "ax[0].set_xticklabels([NEG_WORD,POS_WORD]); ax[0].set_yticklabels([NEG_WORD,POS_WORD])\n",
    "for (i,j),v in np.ndenumerate(cm): ax[0].text(j,i,f'{v:,}',ha='center',va='center')\n",
    "# (2) ROC and PR curves\n",
    "fpr,tpr,_ = roc_curve(yte, pb); prec,rec,_ = precision_recall_curve(yte, pb)\n",
    "ax[1].plot(fpr,tpr,color='#264653'); ax[1].plot([0,1],[0,1],'--',c='grey')\n",
    "ax[1].set_title(f'ROC (AUC={roc_auc_score(yte,pb):.4f})'); ax[1].set_xlabel('FPR'); ax[1].set_ylabel('TPR')\n",
    "ax[2].plot(rec,prec,color='#e76f51'); ax[2].set_title('Precision-Recall'); ax[2].set_xlabel('recall'); ax[2].set_ylabel('precision')\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# (3) feature importances + (4) per-attack-family recall\n",
    "fig, ax = plt.subplots(1, 2, figsize=(13, 5))\n",
    "imp, names = None, feat                                           # importances, robust to the scaled-LR pipeline\n",
    "if hasattr(best, 'feature_importances_'):                          # tree models\n",
    "    imp = best.feature_importances_; names = list(getattr(best, 'feature_names_in_', feat))[:len(imp)]\n",
    "elif hasattr(best, 'named_steps') and 'logisticregression' in getattr(best, 'named_steps', {}):\n",
    "    imp = np.abs(best.named_steps['logisticregression'].coef_[0]); names = feat  # LR pipeline\n",
    "elif hasattr(best, 'coef_'):\n",
    "    imp = np.abs(best.coef_[0]); names = feat\n",
    "if imp is not None:\n",
    "    pd.Series(imp, index=names[:len(imp)]).sort_values().tail(12).plot.barh(ax=ax[0], color='#264653')\n",
    "ax[0].set_title(f'{best_name}: top importances / |coef|')\n",
    "# Per-family recall, WORST-first so rare, hard classes are visible, not just the dominant floods.\n",
    "fam_te = df.loc[Xte.index, 'family']\n",
    "fr = {}\n",
    "for fam, cnt in fam_te[yte==1].value_counts().items():\n",
    "    if cnt < 5: continue                                          # need a few positives for a meaningful recall\n",
    "    mask = (fam_te==fam).to_numpy(); fr[fam] = recall_score(yte[mask], pred[mask], zero_division=0)\n",
    "srt = pd.Series(fr).sort_values()\n",
    "show = pd.concat([srt.head(9), srt.tail(3)]) if len(srt) > 12 else srt   # worst 9 + best 3\n",
    "show = show[~show.index.duplicated()]\n",
    "show.plot.barh(ax=ax[1], color=['#e76f51' if v < 0.5 else '#2a9d8f' for v in show]); ax[1].set_xlim(0,1)\n",
    "ax[1].set_title('Per-family recall (worst first; red < 0.5)')\n",
    "plt.tight_layout(); plt.show()\n",
    "# Operational numbers, not just figures: false-positive rate and the worst per-family recalls.\n",
    "tn, fp = int(cm[0,0]), int(cm[0,1])\n",
    "fpr_op = fp/(fp+tn) if (fp+tn) > 0 else float('nan')             # benign wrongly flagged @0.5\n",
    "print(f'operational FALSE-POSITIVE RATE @0.5 = {fpr_op:.4f}  ({fp:,} benign flagged of {fp+tn:,})')\n",
    "print('worst per-family recalls:', {k: round(v, 3) for k, v in srt.head(6).items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57362636",
   "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.\n",
    "\n",
    "**A shortcut none of this removes:** `Dst Port` stays in the feature matrix. The day's attacks are FTP brute force (port 21) and SSH brute force (port 22). Because of that, the destination port is close to an identifier of the attack class on this capture, not evidence about how the traffic behaves. The panel below ranks `Dst Port` second, behind `Fwd Seg Size Min`. The grade printed here does not account for it, and the ablation in section 11 drops only the top-ranked feature. So no number in this notebook shows what the model scores with the port column removed. Treat that as an untested part of the headline, not a cleared one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49420681",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.9984  (feature: Fwd Seg Size Min)\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) = 0.356\n",
      "TRAIN/TEST exact-row contamination       = 0.329  (single-feat grade D, contam grade D)\n",
      "==> data trust grade: D   (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": "6ae0f1ad",
   "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": "17b6261e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ablation \u2014 how much of the headline survives once each artifact is removed:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>setting</th>\n",
       "      <th>held_out_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>headline (as-is)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (36% rows removed)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (Fwd Seg Size Min)</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       setting  held_out_auc\n",
       "0                             headline (as-is)           1.0\n",
       "1             de-duplicated (36% rows removed)           1.0\n",
       "2  shortcut feature dropped (Fwd Seg Size Min)           1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Ablation: SHOW the inflation empirically, don't just narrate it ---\n",
    "from sklearn.base import clone\n",
    "def _retrain_auc(Xa, ya):                                        # re-split, stratified-subsample, refit best family\n",
    "    xtr, xte, ytr2, yte2 = train_test_split(Xa, ya, test_size=0.25, random_state=RANDOM_STATE, stratify=ya)\n",
    "    if len(xtr) > 120_000:\n",
    "        xtr, _, ytr2, _ = train_test_split(xtr, ytr2, train_size=120_000, random_state=RANDOM_STATE, stratify=ytr2)\n",
    "    m = clone(best); m.fit(xtr, ytr2)\n",
    "    return roc_auc_score(yte2, m.predict_proba(xte)[:, 1])\n",
    "base_auc = roc_auc_score(yte, best.predict_proba(Xte)[:, 1])     # (0) the headline held-out AUC\n",
    "Xdd = X.drop_duplicates(); ydd = y[Xdd.index]                    # (1) de-duplicated corpus\n",
    "auc_dedup = _retrain_auc(Xdd, ydd)\n",
    "auc_noshort = _retrain_auc(X.drop(columns=[best_col]), y) if best_col in X.columns else base_auc  # (2) drop shortcut\n",
    "ablation = pd.DataFrame([\n",
    "    {'setting': 'headline (as-is)',              'held_out_auc': round(base_auc, 6)},\n",
    "    {'setting': f'de-duplicated ({1-len(Xdd)/len(X):.0%} rows removed)', 'held_out_auc': round(auc_dedup, 6)},\n",
    "    {'setting': f'shortcut feature dropped ({best_col})', 'held_out_auc': round(auc_noshort, 6)},\n",
    "])\n",
    "print('Ablation \u2014 how much of the headline survives once each artifact is removed:')\n",
    "ablation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed31cd95",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2801ef45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest 3-fold CV ROC-AUC = 1.0000 +/- 0.0000  (mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)\n"
     ]
    }
   ],
   "source": [
    "# --- Reproducibility & robustness ---\n",
    "import sklearn\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
    "print(f'seed={RANDOM_STATE} | numpy {np.__version__} | sklearn {sklearn.__version__} | '\n",
    "      f'xgboost {xgb.__version__} | lightgbm {lgb.__version__}')\n",
    "# 3-fold cross-validated ROC-AUC of the winning model (fresh clone, bounded subsample) -> mean +/- std.\n",
    "from sklearn.base import clone\n",
    "cvX, cvy = Xtr.iloc[:40_000], ytr[:40_000]\n",
    "def _auc_scorer(est, Xv, yv):                                   # robust to xgboost's 2-col predict_proba\n",
    "    p = est.predict_proba(Xv)\n",
    "    p = p[:, 1] if getattr(p, 'ndim', 1) == 2 else p\n",
    "    return roc_auc_score(yv, p)\n",
    "try:\n",
    "    cv = cross_val_score(clone(best), cvX, cvy,\n",
    "                         cv=StratifiedKFold(3, shuffle=True, random_state=RANDOM_STATE),\n",
    "                         scoring=_auc_scorer, error_score='raise')\n",
    "    assert np.all(np.isfinite(cv)), 'non-finite CV folds'   # FAIL CLOSED: never narrate a NaN as evidence\n",
    "    print(f'{best_name} 3-fold CV ROC-AUC = {cv.mean():.4f} +/- {cv.std():.4f}  '\n",
    "          f'(mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)')\n",
    "except Exception as e:\n",
    "    print(f'CV UNAVAILABLE ({type(e).__name__}: {str(e)[:60]}); rely on the single held-out AUC above \u2014 '\n",
    "          f'we do NOT report a CV number we could not compute')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21179c7",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "Four learners reach near-perfect ROC-AUC on this day's flows. The ledger below names the actual strongest single feature and reports what happens when it is removed \u2014 read that before assuming a service-fingerprint story. What this capture certainly shows is that brute-force traffic is trivially separable *in this recording*. Whether that reflects attack behaviour or how CSE-CIC-IDS2018 was generated is precisely what the CICFlowMeter-defect literature disputes (Engelen et al., 2021; Rosay et al., 2022). One in-distribution score cannot settle it.\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.6367**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **RandomForest** (3-fold CV ROC-AUC **1.0000**). Strongest *single* feature: `Fwd Seg Size Min` at AUC **0.9984**. The ablation refutes a `Fwd Seg Size Min` story. Dropping that feature barely moves the AUC: **1.000000 \u2192 1.000000**. So the separability is **multi-feature**. That reflects how this corpus was generated rather than that one column. Read *multi-feature* narrowly, though. `Dst Port` was kept as a feature and was dropped in no ablation. This day's attacks are FTP brute force (port 21) and SSH brute force (port 22). On such a day, `Dst Port` is close to a name for the attack class. So part of the surviving separability may be the port rather than behaviour. The single-feature panel ranks it second, behind `Fwd Seg Size Min`. De-duplication does **not** lower the score (**1.000000**). So duplicate rows are not what props it up. The overlap above is a caveat about the *random split*, not proof the score is fabricated. Data-trust grade: **D**. It is the worse of two independent sub-checks. Single-feature AUC 0.9984 scores **D**. Train/test exact-row overlap 0.329 scores **D**. Both sub-checks land on the same grade. 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: **0.0000**. Worst per-group recalls, exactly as printed: {`SSH-Bruteforce`: 1.0, `FTP-BruteForce`: 1.0}. Every group listed is recovered essentially perfectly. So there is no rare-class failure to report on this split. That uniformity suggests the corpus is easy to separate, not that the detector is strong. **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": "8e797c1c",
   "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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