{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5f4f82f",
   "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 denial-of-service day, then grade the evidence behind the score.\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": "c54f2080",
   "metadata": {},
   "source": [
    "# CIC-IDS2018 Case Study \u2014 DoS (GoldenEye / Slowloris)\n",
    "### Model comparison + validity audit on the dos (goldeneye / slowloris) day of CSE-CIC-IDS2018\n",
    "\n",
    "**Abstract:** We study one day of the CSE-CIC-IDS2018 flow corpus. Its attack traffic is **dos (goldeneye / slowloris)**. Benign flows still dominate the day numerically: the loader prints 1,048,575 flows at an attack rate of 0.0501. The four learners **train** on a 120,000-row stratified subsample of the training half. But they are **scored on the whole 262,144-row held-out split**. So the accuracy and ROC-AUC figures below are full-holdout measurements of a subsample-*trained* model, not scores estimated on 120,000 rows. The question is whether the near-perfect in-distribution scores reflect detection, or the CICFlowMeter defects that Engelen et al. (2021) documented on **CICIDS2017**. The feature extractor is shared with this 2018 capture. Their corrections to individual flow labels are not shared with this capture, and nothing here re-audits the 2018 labels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22177aea",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Detect DoS-GoldenEye and DoS-Slowloris connections on the 2018-02-15 capture. These low-and-slow / flooding attacks perturb flow-timing features. We do **not** run a controlled volume-versus-timing experiment here. We report which features the model leans on and whether the score survives dropping the strongest one. We also ask whether models learn timing signatures or shortcut on volume."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef40feba",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Sharafaldin, Lashkari & Ghorbani (2018)** \u2014 the **CIC-IDS2017** dataset paper (ICISSP 2018, 108\u2013116): B-Profile-generated benign traffic, a labelled attack schedule, and the 80-column CICFlowMeter feature set. CIC asks users of CSE-CIC-IDS2018 to cite it, but it is **not** a description of this 2018 capture. CSE-CIC-IDS2018 is a separate CSE\u2013CIC collaboration run on AWS. It inherits the generation methodology and the feature extractor. It does not inherit the network, the hosts, the schedule or the labels.\n",
    "- **Engelen, Rimmer & Joosen (2021)** \u2014 *Troubleshooting an Intrusion Detection Dataset: the CICIDS2017 Case Study* (IEEE S&P Workshops). The paper found labelling errors and CICFlowMeter implementation bugs that inflate scores. **Read the scope:** the audit is of **CICIDS2017**. The extractor bugs are a reasonable suspicion here, because the 2018 CSVs were produced with the same CICFlowMeter. The specific mislabelled flows they corrected do **not** transfer to this day. Nothing below re-audits the 2018 labels.\n",
    "- **Rosay, Cheval, Carlier & Leroux (2022)** \u2014 *Network Intrusion Detection: A Comprehensive Analysis of CIC-IDS2017* (ICISSP): documents CICFlowMeter implementation flaws in these flow features. The paper again measured these flaws on the 2017 corpus, so treat the *mechanism* as transferable and the *measurements* as not.\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",
    "**Representative approaches and their known caveats** \u2014 drawn from the wider literature and qualitative only. These are **not** measurements reproduced on this corpus, so no figures are quoted.\n",
    "\n",
    "| Reported approach | Known caveat |\n",
    "|---|---|\n",
    "| Sharafaldin et al. (2018) \u2014 classifier baselines on CIC-IDS**2017** | in-distribution; CICFlowMeter features later shown buggy; a different capture from this day |\n",
    "| Engelen et al. (2021) \u2014 re-labelled CICIDS2017 | original 2017 labels and features partly wrong; no equivalent re-labelling exists for 2018 |\n",
    "| Typical DL-NIDS papers | benign-majority base rate inflates accuracy; per-family recall varies |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e9057f0",
   "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 | `Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv` |\n",
    "| Rows | **1,048,575** flow records, as printed by the loader. The download caps the stream at 1.2 M lines; this day's CSV is smaller than the cap, so the cap does not bind. |\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 (`DoS attacks-GoldenEye`, `DoS attacks-Slowloris`) |\n",
    "| Attack rate | **0.0501**, printed by the loader |\n",
    "\n",
    "**Honestly:** CICFlowMeter's feature implementation has documented bugs, but the documentation is of **CICIDS2017** (Engelen et al. 2021; Rosay et al. 2022), not of this 2018 day. The extractor is the same, so the feature-level defects are a live suspicion here; the label corrections those authors published are 2017-specific and are **not** applied below. Accuracy is separately inflated by the benign-majority base rate. We report per-attack-family recall and audit single-feature shortcuts for those reasons.\n",
    "\n",
    "**A shortcut deliberately named, not removed \u2014 `Dst Port`:** The loader's drop list is only `Label`, `Timestamp`, `y` and `family`. So `Dst Port` and `Protocol` survive into `X`. Both of this day's tools are HTTP denial-of-service attacks aimed at a single victim host. CIC's published 2018-02-15 schedule runs GoldenEye 09:26\u201310:09 and Slowloris 10:59\u201311:40 against the same target. Benign traffic on the same day spreads across DNS, HTTPS, RDP, SMB and more. Destination port therefore acts as a cheap **negative-class filter**. It encodes the capture schedule rather than attack behaviour. A model can discard a large share of benign flows without learning anything about denial of service. This notebook does **not** quantify that effect. Nor is `Dst Port` the shortcut the section-10 audit surfaces (it prints `Fwd Seg Size Min` as the strongest single feature). But the column is in the matrix, and a deployment-realistic rerun would drop it before any of the numbers below are read.\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": "e306eb46",
   "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": "4441b73c",
   "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": "be72050f",
   "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": "db6c6b24",
   "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": "f971a3c9",
   "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": "fc5472a3",
   "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": "ea2f0e49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 1,048,575 flows x 68 features; attack rate 0.0501\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "FILE = 'Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv'; SHORT = 'dos'\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": "7f91e605",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a5944480",
   "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": "b02f8d30",
   "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": "4e97ab0e",
   "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, drawn from the training half of a 75/25 stratified split. They are then **scored on the entire held-out half**. The cell prints both counts. The held-out half (262,144 rows) is more than twice the size of the training subsample. So the figures below are full-holdout numbers from a subsample-*trained* model: the training set is bounded, the evaluation set is not. (The inline comment in the cell calls them \"SUBSAMPLE\" numbers; read that as subsample-**trained**. What the cap limits is how much the learners could fit, not how many rows the score was measured on.)\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": "60b028b5",
   "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 = 0.9499  (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>0.999908</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.999939</td>\n",
       "      <td>0.999992</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.999416</td>\n",
       "      <td>0.999925</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.949900</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  0.999908  1.000000      0.8\n",
       "1             XGBoost  0.999939  0.999999      0.4\n",
       "2            LightGBM  0.999939  0.999992      1.1\n",
       "3  LogisticRegression  0.999416  0.999925      0.3\n",
       "4    MajorityBaseline  0.949900  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": "3e35d901",
   "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": "0c6ad15a",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1560x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operational FALSE-POSITIVE RATE @0.5 = 0.0000  (5 benign flagged of 249,019)\n",
      "worst per-family recalls: {'DoS attacks-Slowloris': 0.996, 'DoS attacks-GoldenEye': 0.999}\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": "d6444528",
   "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": "685f3878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.9852  (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) = 0.156\n",
      "TRAIN/TEST exact-row contamination       = 0.104  (single-feat grade C, contam grade B)\n",
      "==> data trust grade: C   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Validity audit: is the score real detection, or a data shortcut? ---\n",
    "from sklearn.metrics import roc_auc_score\n",
    "samp = X.sample(min(60_000, len(X)), random_state=1); ysamp = y[samp.index]\n",
    "aucs = {}\n",
    "for c in feat:                                                    # AUC of EACH feature alone\n",
    "    col = samp[c].to_numpy(float)\n",
    "    if col.std()==0: continue\n",
    "    a = roc_auc_score(ysamp, col); aucs[c] = max(a, 1-a)          # direction-agnostic\n",
    "best_auc = max(aucs.values()); best_col = max(aucs, key=aucs.get)\n",
    "dup_rate = 1 - X.drop_duplicates().shape[0]/len(X)               # exact-duplicate feature rows (whole set)\n",
    "# The statistic that actually inflates a held-out score is TRAIN/TEST CONTAMINATION: how many test\n",
    "# rows are exact duplicates of a training row. Measure it directly on the split used above.\n",
    "_trkeys = set(map(tuple, np.round(Xtr.to_numpy(), 6)))\n",
    "_te = np.round(Xte.to_numpy(), 6)[:50_000]\n",
    "contam = float(np.mean([tuple(r) in _trkeys for r in _te]))      # fraction of test rows seen in train\n",
    "# Trust grade reflects BOTH failure modes and takes the WORSE of the two: a near-perfect single\n",
    "# feature (shortcut) OR heavy train/test contamination each independently invalidate the headline.\n",
    "_ga = 'F' if best_auc>=0.999 else 'D' if best_auc>=0.99 else 'C' if best_auc>=0.95 else 'B' if best_auc>=0.85 else 'A'\n",
    "_gc = 'F' if contam>=0.5 else 'D' if contam>=0.3 else 'C' if contam>=0.15 else 'B' if contam>=0.05 else 'A'\n",
    "grade = max(_ga, _gc)                                            # 'max' letter = worse grade (A best, F worst)\n",
    "print(f'best single-feature AUC = {best_auc:.4f}  (feature: {best_col})')\n",
    "print(f'   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\\n'\n",
    "      f'   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.')\n",
    "print(f'exact-duplicate row rate (whole corpus) = {dup_rate:.3f}')\n",
    "print(f'TRAIN/TEST exact-row contamination       = {contam:.3f}  (single-feat grade {_ga}, contam grade {_gc})')\n",
    "print(f'==> data trust grade: {grade}   (worse of the two; F = shortcut and/or heavy contamination)')\n",
    "s = pd.Series(aucs).sort_values().tail(15)\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "s.plot.barh(ax=ax, color=['#e76f51' if v>=0.99 else '#457b9d' for v in s]); ax.axvline(0.5,ls='--',c='grey')\n",
    "ax.set_xlim(0.5,1.0); ax.set_title('Single-feature ROC-AUC (red = near-perfect shortcut)'); ax.set_xlabel('AUC alone')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3477ae1",
   "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": "34f5ac3e",
   "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>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (16% rows removed)</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (Fwd Seg Size Min)</td>\n",
       "      <td>0.999769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       setting  held_out_auc\n",
       "0                             headline (as-is)      1.000000\n",
       "1             de-duplicated (16% rows removed)      1.000000\n",
       "2  shortcut feature dropped (Fwd Seg Size Min)      0.999769"
      ]
     },
     "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": "f2fda30e",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be75f20a",
   "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": "fa33f7d4",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "Per-family recall is high for both GoldenEye and Slowloris in-distribution \u2014 but that says the *capture* is separable, not that the detector generalizes.\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** every score here was measured on the **full 262,144-row held-out split**. Only the *training* set was capped at a 120,000-row stratified subsample. Majority-class baseline **accuracy**: **0.9499**. 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 **0.9852**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **1.000000 \u2192 0.999769**. So the separability is **multi-feature**. De-duplication does **not** lower the score either (**1.000000**), so duplicate rows are not what props it up, even though **0.156** of the corpus rows are exact duplicates. Overlap is not heavy, but it is **not negligible either** (grade B). The random split still flatters the headline a little. Data-trust grade: **C**. It is the worse of two independent sub-checks. Single-feature AUC 0.9852 scores **C**. Train/test exact-row overlap 0.104 scores **B**. The single-feature check drives the grade, not the overlap check. Operational false-positive rate at threshold 0.5: **0.0000**. Worst per-group recalls, exactly as printed: {`DoS attacks-Slowloris`: 0.996, `DoS attacks-GoldenEye`: 0.999}. The weakest group sits at **0.996**, which is where detection is thinnest.\n",
    "\n",
    "**What the multi-feature result does and does not license:** Many redundant columns separate the classes, so no single leaky column explains the score. That is a statement about how this corpus was generated \u2014 two attack tools, one victim, two narrow time windows, set against profiled synthetic benign traffic. This is not a clean bill of health. It is also **not** a reproduction of the CICFlowMeter bug findings: Engelen et al. (2021) and Rosay et al. (2022) audited **CICIDS2017**, and nothing above re-measures their defects, or their label corrections, on this 2018 day. The shared extractor is grounds for suspicion, not evidence.\n",
    "\n",
    "**A shortcut left in the matrix:** `Dst Port` was never dropped. Both attacks are HTTP floods against one victim host. So the port column separates much of the benign traffic from the attack for free, with no denial-of-service behaviour learned. It is not the feature the single-feature audit ranks first. But it is a capture artifact sitting in `X`. For that reason alone, every number above should be read as an upper bound.\n",
    "\n",
    "**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": "6bb6a33b",
   "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. *4th Int. Conf. on Information Systems Security and Privacy (ICISSP)*, 108\u2013116. \u2014 the **CIC-IDS2017** dataset paper. CIC also asks users of CSE-CIC-IDS2018 to cite it, but it does not describe that later capture.\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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