{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e4f236a",
   "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 login-risk detectors on the RBA corpus, where the label is a property of the source IP.\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. Recognise circularity when the label is a property of a feature.\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 4: Attack Graph Analytics** \u2014 Learning objective 5 (section 4.1) separates a **one-at-a-time** perturbation from the smallest perturbation that reverses a ranking, and warns the first **overstates stability**. Dropping only the top feature and refitting is exactly that weaker test, so read it as a floor.\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": "e36afede",
   "metadata": {},
   "source": [
    "# Credential-Attack Detection on the RBA Login Dataset\n",
    "### Model comparison + validity audit on \u22651M login attempts (RBA synthesized release, 31.3M total)\n",
    "\n",
    "**Abstract:** The RBA dataset (Wiefling et al., 2022) is **31.3 million login attempts** to a large online service. Each attempt is labelled `Is Attack IP` (the login came from a known-attacker IP) and `Is Account Takeover`. Only `Is Attack IP` is modelled here. It is a risk-based-authentication problem: flag malicious logins from client and network metadata. We keep every attack login and subsample benign to \u22651M, and compare four learners. We audit how much of the score is genuine risk signal, versus a consequence of the label being an IP-list property."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83d7ecd",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Classify a login attempt as coming from an **attack IP** vs benign using round-trip time, geolocation (country/region/city/ASN), and client strings (browser, OS, device). This is the core of adaptive / risk-based authentication used to trigger step-up MFA. The subtlety: the label is defined by IP-blocklist membership, so any IP-derived feature can *circularly* predict it \u2014 the audit exists to expose that."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b523b55e",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Wiefling, J\u00f8rgensen, Thunem & Lo Iacono (2022)** \u2014 *Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service* (ACM TOPS): the source study. The public release is a **synthesized** reconstruction of that traffic (so customers cannot be re-identified); the full public file holds **31,269,264** login records. This notebook reads a bounded slice of it: the loader prints **6,477,653** rows. Every number below is measured on that slice.\n",
    "- **Freeman, Jain, D\u00fcrmuth, Biggio & Giacinto (2016)** \u2014 *Who Are You? A Statistical Approach to Measuring User Authenticity* (NDSS): the RBA risk-scoring model.\n",
    "- **Thomas et al. (2017)** \u2014 *Data Breaches, Phishing, or Malware? Understanding the Risks of Stolen Credentials* (ACM CCS): measuring the credential-theft ecosystem.\n",
    "- **Sommer & Paxson (2010)** \u2014 the closed-world critique.\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",
    "| Wiefling et al. (2022) RBA scoring | IP/ASN reputation carries much of it |\n",
    "| Freeman et al. (2016) authenticity model | features overlap with the labelling signal |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d0ccaf",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `dasgroup/rba-dataset` (Wiefling et al., 2022) |\n",
    "| Rows | 31.3M logins; **every attack row kept + benign subsampled to \u22651M** |\n",
    "| Label | `Is Attack IP` (the only target used); `Is Account Takeover` (loaded, then dropped, never modelled) |\n",
    "| Access | Kaggle API token required (~9 GB one-time download) |\n",
    "\n",
    "**Honestly:** the label is a property of the source **IP**, so we drop the IP address, User ID and timestamp as direct leaks \u2014 but geolocation and ASN are *also* IP-derived, so a high score partly reflects that attack IPs cluster in a few networks/countries in this capture. Whether that transfers to a *new* campaign from different ASNs is the real question. Per-country recall additionally exposes geographic bias.\n",
    "\n",
    "**On `Is Account Takeover`:** it is never evaluated in this notebook. The loader reads the column, then lists it in `DROP`. So it is neither a feature nor a target, and no ATO score appears anywhere below. Treat it as an unused second label, not a held-out test set. It does stay on `df`, so a student can start there. Rate in this bounded sample: `df['Is Account Takeover'].astype(str).str.lower().isin(['true','1']).mean()`. Swapping it for `y` changes the question from *did this login come from a blocklisted IP?* to *did this login actually take over an account?* That second label is not IP-derived, so the circularity described above does not apply to it. Whether it is learnable at all from these eight features is an untested hypothesis here.\n",
    "\n",
    "### Before you run this: getting the data\n",
    "\n",
    "This notebook downloads its own data on the first run, then caches it. You do not fetch anything by hand.\n",
    "\n",
    "**Dataset:** Kaggle `dasgroup/rba-dataset` -> `/tmp/kg_rba`. It is about **8 GB** on disk.\n",
    "\n",
    "**One-time setup.** Sign in at kaggle.com, open **Settings**, and under **API** choose **Create New Token**. Kaggle hands you a `kaggle.json` file. This notebook does *not* read that file. It reads a plain key file, so convert it once:\n",
    "\n",
    "```bash\n",
    "mkdir -p ~/.kaggle\n",
    "python3 -c \"import json;print(json.load(open('kaggle.json'))['key'],end='')\" > ~/.kaggle/access_token\n",
    "chmod 600 ~/.kaggle/access_token\n",
    "```\n",
    "\n",
    "Never paste the token into a cell, a commit, or a screenshot. If it leaks, revoke it from the same Settings page.\n",
    "\n",
    "**If the loader fails:**\n",
    "\n",
    "- `FileNotFoundError: ~/.kaggle/access_token` - you created `kaggle.json` but not the key file. Run the command above.\n",
    "- `401 Unauthorized` - the key is wrong, or a trailing newline crept in.\n",
    "- `403 Forbidden` - open the dataset page on Kaggle while signed in, accept its terms, then re-run the cell.\n",
    "\n",
    "The cache sits under `/tmp`, which macOS clears on reboot. To keep it, move the folder somewhere durable and symlink it back. Do **not** edit the path in the code cell below: that changes a code cell and invalidates the stored outputs you are reviewing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8106a0c",
   "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": "aaf22424",
   "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": "30b941c8",
   "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": "93a360a8",
   "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": "8ba5c22d",
   "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`. Here `family` is the per-group label used for the recall breakdown. On this corpus `family` is set from the `Country` column. It is therefore a **geographic stratum, not an attack family**. The variable keeps the name `family` only because every notebook in this series shares one plotting path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "138c3e54",
   "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": "599c6ebc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 6,477,653 login attempts x 8 features; attack-IP rate (bounded sample) 0.4781\n"
     ]
    }
   ],
   "source": [
    "import os, glob, re\n",
    "# RBA (Wiefling, J\u00f8rgensen, Thunem & Lo Iacono, 2022): 31.3M login attempts to a large online\n",
    "# service. The public Kaggle release is a SYNTHESIZED version of the original production traffic\n",
    "# (re-generated so customers cannot be re-identified), so it mirrors real behaviour but is not\n",
    "# itself the raw log,\n",
    "# labelled `Is Attack IP` (login from a known-attacker IP) and `Is Account Takeover`. A risk-based-\n",
    "# authentication / credential-attack detection problem. Self-contained Kaggle download (~9 GB).\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "RBA_DIR = '/tmp/kg_rba'; os.makedirs(RBA_DIR, exist_ok=True)\n",
    "hits = glob.glob(RBA_DIR + '/**/rba-dataset.csv', recursive=True)\n",
    "if not hits:\n",
    "    import kaggle; kaggle.api.authenticate()\n",
    "    print('downloading RBA (~9 GB, one-time)...')\n",
    "    kaggle.api.dataset_download_files('dasgroup/rba-dataset', path=RBA_DIR, unzip=True, quiet=True)\n",
    "    hits = glob.glob(RBA_DIR + '/**/rba-dataset.csv', recursive=True)\n",
    "f = hits[0]; assert os.path.exists(f), f'RBA csv not found: {f}'\n",
    "# Read in CHUNKS (the User-Agent field contains commas, so proper CSV quoting matters, and the file\n",
    "# is 9 GB). We DROP the IP Address, User ID and timestamp: `Is Attack IP` is literally a property of\n",
    "# the IP, so those columns would be direct label leakage. We keep geo / ASN / client-string features\n",
    "# and let the audit reveal how much the label rides on IP-derived metadata. Attack IPs are ~10% and\n",
    "# time-clustered, so we KEEP EVERY attack row and SUBSAMPLE benign (bounded-sample rate, not base rate).\n",
    "USE = ['Round-Trip Time [ms]','Country','Region','City','ASN','Browser Name and Version',\n",
    "       'OS Name and Version','Device Type','Is Attack IP','Is Account Takeover']\n",
    "frames = []\n",
    "for ch in pd.read_csv(f, usecols=USE, chunksize=3_000_000, low_memory=False):\n",
    "    ch.columns = [c.strip() for c in ch.columns]\n",
    "    ap = ch['Is Attack IP'].astype(str).str.lower().isin(['true', '1'])\n",
    "    neg = ch[~ap].sample(frac=0.12, random_state=0)\n",
    "    frames.append(pd.concat([ch[ap], neg]))\n",
    "df = pd.concat(frames, ignore_index=True)\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'\n",
    "df['y'] = df['Is Attack IP'].astype(str).str.lower().isin(['true', '1']).astype(int)\n",
    "# family = per-country grouping, so per-family recall exposes GEOGRAPHIC detection bias.\n",
    "df['family'] = df['Country'].astype(str).fillna('??')\n",
    "DROP = ['y', 'family', 'Is Attack IP', 'Is Account Takeover']\n",
    "feat = [c for c in df.columns if c not in DROP]\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X = df[feat].copy()\n",
    "for c in X.select_dtypes(include='object').columns:\n",
    "    X[c] = LabelEncoder().fit_transform(X[c].astype(str))\n",
    "X = X.apply(pd.to_numeric, errors='coerce').replace([np.inf, -np.inf], np.nan).fillna(0.0)\n",
    "X = X.loc[:, X.nunique() > 1]\n",
    "X.columns = [re.sub(r'[^0-9A-Za-z_]+', '_', str(c)).strip('_') or f'f{i}'\n",
    "             for i, c in enumerate(X.columns)]                 # LightGBM-safe names ([ms], spaces)\n",
    "feat = list(X.columns)\n",
    "y = df['y'].to_numpy(); family = df['family'].to_numpy()\n",
    "print(f'loaded {len(df):,} login attempts x {len(feat)} features; '\n",
    "      f'attack-IP rate (bounded sample) {y.mean():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7eda06a",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis\n",
    "\n",
    "**Figure label warning:** the right-hand panel below is titled *Top attack families*. It is not an attack taxonomy. It plots the top **source countries** among attack-IP logins, because `family` holds the two-letter country code. The title is frozen in a saved output and the code is left unchanged, so read it as *Top attack source countries*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0dc72e1",
   "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": "61831b86",
   "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": "00469b3e",
   "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": "3994b726",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 6,477,653 rows | trained on 120,000 (stratified subsample) | held-out 1,619,414\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.5219  (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>0.859547</td>\n",
       "      <td>0.909410</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.857329</td>\n",
       "      <td>0.908066</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.859579</td>\n",
       "      <td>0.907558</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.786163</td>\n",
       "      <td>0.815961</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.521900</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  0.859547  0.909410      0.5\n",
       "1            LightGBM  0.857329  0.908066      1.5\n",
       "2        RandomForest  0.859579  0.907558      1.3\n",
       "3  LogisticRegression  0.786163  0.815961      0.3\n",
       "4    MajorityBaseline  0.521900  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": "8b97cfa7",
   "metadata": {},
   "source": [
    "## 9. Results\n",
    "\n",
    "Diagnostics for the winning model, including **per-group recall**.\n",
    "\n",
    "**The groups are countries, not attack families:** the loader sets `family` from the `Country` column, so each group is a two-letter country code (ISO 3166-1 alpha-2). In the worst-recall list below, `SM` is San Marino, `KH` Cambodia, `HN` Honduras, `GR` Greece, `JM` Jamaica and `JO` Jordan. This corpus has one attack class, not a taxonomy of them. The breakdown therefore measures **geographic** detection bias, which is what \u00a73 warned about.\n",
    "\n",
    "**Frozen labels:** the right-hand panel of the second figure is titled *Per-family recall* and the printed line reads *worst per-family recalls*. Both are stratified by country. The code is left unedited so the saved outputs stay reproducible. Read \"family\" as \"country\" everywhere in this section.\n",
    "\n",
    "Read the bars accordingly. A near-zero bar means the model misses attack logins from that country. It does not mean it misses an attack technique. Countries with fewer than five held-out attack logins are skipped by the plotting code, so every visible bar has at least a little evidence behind it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8096818",
   "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 = 0.2111  (178,440 benign flagged of 845,169)\n",
      "worst per-family recalls: {'SM': 0.0, 'KH': 0.0, 'HN': 0.0, 'GR': 0.0, 'JM': 0.0, 'JO': 0.0}\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": "1654924f",
   "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": "9cbfea41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.8095  (feature: ASN)\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) = 0.897\n",
      "TRAIN/TEST exact-row contamination       = 0.737  (single-feat grade A, contam grade F)\n",
      "==> data trust grade: F   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Validity audit: is the score real detection, or a data shortcut? ---\n",
    "from sklearn.metrics import roc_auc_score\n",
    "samp = X.sample(min(60_000, len(X)), random_state=1); ysamp = y[samp.index]\n",
    "aucs = {}\n",
    "for c in feat:                                                    # AUC of EACH feature alone\n",
    "    col = samp[c].to_numpy(float)\n",
    "    if col.std()==0: continue\n",
    "    a = roc_auc_score(ysamp, col); aucs[c] = max(a, 1-a)          # direction-agnostic\n",
    "best_auc = max(aucs.values()); best_col = max(aucs, key=aucs.get)\n",
    "dup_rate = 1 - X.drop_duplicates().shape[0]/len(X)               # exact-duplicate feature rows (whole set)\n",
    "# The statistic that actually inflates a held-out score is TRAIN/TEST CONTAMINATION: how many test\n",
    "# rows are exact duplicates of a training row. Measure it directly on the split used above.\n",
    "_trkeys = set(map(tuple, np.round(Xtr.to_numpy(), 6)))\n",
    "_te = np.round(Xte.to_numpy(), 6)[:50_000]\n",
    "contam = float(np.mean([tuple(r) in _trkeys for r in _te]))      # fraction of test rows seen in train\n",
    "# Trust grade reflects BOTH failure modes and takes the WORSE of the two: a near-perfect single\n",
    "# feature (shortcut) OR heavy train/test contamination each independently invalidate the headline.\n",
    "_ga = 'F' if best_auc>=0.999 else 'D' if best_auc>=0.99 else 'C' if best_auc>=0.95 else 'B' if best_auc>=0.85 else 'A'\n",
    "_gc = 'F' if contam>=0.5 else 'D' if contam>=0.3 else 'C' if contam>=0.15 else 'B' if contam>=0.05 else 'A'\n",
    "grade = max(_ga, _gc)                                            # 'max' letter = worse grade (A best, F worst)\n",
    "print(f'best single-feature AUC = {best_auc:.4f}  (feature: {best_col})')\n",
    "print(f'   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\\n'\n",
    "      f'   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.')\n",
    "print(f'exact-duplicate row rate (whole corpus) = {dup_rate:.3f}')\n",
    "print(f'TRAIN/TEST exact-row contamination       = {contam:.3f}  (single-feat grade {_ga}, contam grade {_gc})')\n",
    "print(f'==> data trust grade: {grade}   (worse of the two; F = shortcut and/or heavy contamination)')\n",
    "s = pd.Series(aucs).sort_values().tail(15)\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "s.plot.barh(ax=ax, color=['#e76f51' if v>=0.99 else '#457b9d' for v in s]); ax.axvline(0.5,ls='--',c='grey')\n",
    "ax.set_xlim(0.5,1.0); ax.set_title('Single-feature ROC-AUC (red = near-perfect shortcut)'); ax.set_xlabel('AUC alone')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3d3eaf",
   "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": "477dd534",
   "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>0.909410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (90% rows removed)</td>\n",
       "      <td>0.947699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (ASN)</td>\n",
       "      <td>0.888466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            setting  held_out_auc\n",
       "0                  headline (as-is)      0.909410\n",
       "1  de-duplicated (90% rows removed)      0.947699\n",
       "2    shortcut feature dropped (ASN)      0.888466"
      ]
     },
     "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": "bd56067e",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9fefd44e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n",
      "XGBoost 3-fold CV ROC-AUC = 0.9042 +/- 0.0007  (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": "80e6575d",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "The learners separate attack from benign logins at high AUC. But the validity audit asks how much of that is genuine behavioural risk signal, versus the circularity of a label defined by IP-blocklist membership. The geo/ASN features re-encode the IP. The honest deployment question is cross-campaign transfer: a model that memorises which ASNs attacked in this capture will not flag a fresh campaign from clean infrastructure. Per-country recall further shows the detector is not uniform across regions \u2014 a fairness and coverage concern for real risk-based authentication (Sommer & Paxson, 2010).\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.5219**. 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 **0.9042**). Strongest *single* feature: `ASN` at AUC **0.8095**. Dropping it costs a real but partial amount: **0.909410 \u2192 0.888466**. The feature carries some of the signal, not all of it. De-duplication **raises** the AUC, to **0.947699**. 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: **F**. It is the worse of two independent sub-checks. Single-feature AUC 0.8095 scores **A**. Train/test exact-row overlap 0.737 scores **F**. The overlap check drives the grade, not the single-feature check. That says the split leaks, not that features are clean; the single-feature check separately scores A. 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: **0.2111**. The per-group keys here are **country codes**, not attack families. Worst per-group recalls, exactly as printed: {`SM`: 0.0, `KH`: 0.0, `HN`: 0.0, `GR`: 0.0, `JM`: 0.0, `JO`: 0.0}. The weakest group sits at **0.000**, so the model misses most of it. That gap, not the aggregate score, is the operationally important result. **Disclosed limitation:** categorical columns are integer-encoded before the split. The encoder therefore sees the test set's category values. On an all-numeric corpus that step is a no-op. The mapping never consults the label, so no *label* information leaks. It is still transductive. A deployed system would need an unseen-category bucket. **How the audit numbers are computed:** overlap is measured on the first 50,000 held-out rows, so read it as a sampled estimate. Each ablation re-splits and refits, so tiny differences are re-split noise. The de-duplication variant keeps the first label when a feature vector appears twice. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38ce915e",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Wiefling, S., J\u00f8rgensen, P.R., Thunem, S. & Lo Iacono, L. (2022). Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. *ACM Transactions on Privacy and Security*, 26(1), Article 6 (2023); preprint arXiv:2206.15139 (2022).\n",
    "2. Freeman, D., Jain, S., D\u00fcrmuth, M., Biggio, B. & Giacinto, G. (2016). Who Are You? A Statistical Approach to Measuring User Authenticity. *NDSS*.\n",
    "3. Thomas, K., Li, F., Zand, A., Barrett, J., Ranieri, J., Invernizzi, L., Markov, Y., Comanescu, O., Eranti, V., Moscicki, A., Margolis, D., Paxson, V. & Bursztein, E. (2017). Data Breaches, Phishing, or Malware? Understanding the Risks of Stolen Credentials. *Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS '17)*, 1421\u20131434. DOI 10.1145/3133956.3134067.\n",
    "4. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*."
   ]
  }
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