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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CYB006 Baseline Classifier — Inference Example\n",
"\n",
"End-to-end demo: load the trained XGBoost and PyTorch MLP models from the Hugging Face repo and predict the **user risk tier** (`low` / `medium` / `high`) of an identity from per-user aggregates joined with non-leaky session aggregates.\n",
"\n",
"**This is a baseline reference model**, not a production identity-security platform. See the model card for full metrics and limitations — and importantly, see the **`leakage_diagnostic.json`** for why this baseline targets `user_risk_tier` rather than the README's stated headline use case of threat-actor tier attribution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install --quiet xgboost torch safetensors pandas numpy huggingface_hub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Download model artifacts from Hugging Face"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import hf_hub_download\n",
"\n",
"REPO_ID = \"xpertsystems/cyb006-baseline-classifier\"\n",
"\n",
"files = {}\n",
"for name in [\"model_xgb.json\", \"model_mlp.safetensors\",\n",
" \"feature_engineering.py\", \"feature_meta.json\",\n",
" \"feature_scaler.json\"]:\n",
" files[name] = hf_hub_download(repo_id=REPO_ID, filename=name)\n",
" print(f\" downloaded: {name}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys, os\n",
"fe_dir = os.path.dirname(files[\"feature_engineering.py\"])\n",
"if fe_dir not in sys.path:\n",
" sys.path.insert(0, fe_dir)\n",
"\n",
"from feature_engineering import (\n",
" transform_single, load_meta, INT_TO_LABEL,\n",
" compute_session_aggregates_for_user\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Load models and metadata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import xgboost as xgb\n",
"from safetensors.torch import load_file\n",
"\n",
"meta = load_meta(files[\"feature_meta.json\"])\n",
"with open(files[\"feature_scaler.json\"]) as f:\n",
" scaler = json.load(f)\n",
"\n",
"N_FEATURES = len(meta[\"feature_names\"])\n",
"N_CLASSES = len(meta[\"int_to_label\"])\n",
"print(f\"feature count: {N_FEATURES}\")\n",
"print(f\"class count: {N_CLASSES}\")\n",
"print(f\"label classes: {list(meta['int_to_label'].values())}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xgb_model = xgb.XGBClassifier()\n",
"xgb_model.load_model(files[\"model_xgb.json\"])\n",
"\n",
"# MLP architecture (must match training)\n",
"class RiskTierMLP(nn.Module):\n",
" def __init__(self, n_features, n_classes=3, hidden1=128, hidden2=64, dropout=0.3):\n",
" super().__init__()\n",
" self.net = nn.Sequential(\n",
" nn.Linear(n_features, hidden1),\n",
" nn.BatchNorm1d(hidden1),\n",
" nn.ReLU(),\n",
" nn.Dropout(dropout),\n",
" nn.Linear(hidden1, hidden2),\n",
" nn.BatchNorm1d(hidden2),\n",
" nn.ReLU(),\n",
" nn.Dropout(dropout),\n",
" nn.Linear(hidden2, n_classes),\n",
" )\n",
" def forward(self, x):\n",
" return self.net(x)\n",
"\n",
"mlp_model = RiskTierMLP(N_FEATURES, n_classes=N_CLASSES)\n",
"mlp_model.load_state_dict(load_file(files[\"model_mlp.safetensors\"]))\n",
"mlp_model.eval()\n",
"print(\"models loaded\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Prediction helper"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"MU = np.array(scaler[\"mean\"], dtype=np.float32)\n",
"SD = np.array(scaler[\"std\"], dtype=np.float32)\n",
"\n",
"def predict_risk_tier(user_record: dict) -> dict:\n",
" \"\"\"Predict the user risk tier from a per-user record.\n",
"\n",
" The record should contain per-user aggregates (from user_risk_summary)\n",
" PLUS the session aggregates produced by compute_session_aggregates_for_user.\n",
" See the example record below.\n",
" \"\"\"\n",
" X = transform_single(user_record, meta)\n",
"\n",
" xgb_proba = xgb_model.predict_proba(X)[0]\n",
" xgb_label = INT_TO_LABEL[int(np.argmax(xgb_proba))]\n",
"\n",
" Xs = ((X - MU) / SD).astype(np.float32)\n",
" with torch.no_grad():\n",
" logits = mlp_model(torch.tensor(Xs))\n",
" mlp_proba = torch.softmax(logits, dim=1).numpy()[0]\n",
" mlp_label = INT_TO_LABEL[int(np.argmax(mlp_proba))]\n",
"\n",
" return {\n",
" \"xgboost\": {\n",
" \"label\": xgb_label,\n",
" \"probabilities\": {INT_TO_LABEL[i]: float(p) for i, p in enumerate(xgb_proba)},\n",
" },\n",
" \"mlp\": {\n",
" \"label\": mlp_label,\n",
" \"probabilities\": {INT_TO_LABEL[i]: float(p) for i, p in enumerate(mlp_proba)},\n",
" },\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Run on an example record\n",
"\n",
"Real high-risk user from the sample dataset: 98 login attempts in window, 25 failures, 9 account lockouts, 9 impossible-travel events, 6 unique countries, peak privilege `admin_domain`. Both models should predict `high`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Real per-user record from the sample dataset (true tier: high)\n",
"example_record = {\n",
" # Per-user aggregates (from user_risk_summary.csv)\n",
" \"total_login_attempts\": 98,\n",
" \"successful_logins\": 0,\n",
" \"failed_logins\": 25,\n",
" \"mfa_failures\": 0,\n",
" \"impossible_travel_events\": 9,\n",
" \"lateral_hop_count\": 1,\n",
" \"privilege_escalations\": 1,\n",
" \"account_lockout_count\": 9,\n",
" \"geo_dispersion_score\": 0.6474,\n",
" \"login_velocity_score\": 0.6387,\n",
" \"session_anomaly_rate\": 1.0,\n",
" \"ueba_alert_count\": 0,\n",
" \"overall_identity_risk_score\": 0.3452,\n",
" \"peak_privilege_level_accessed\": \"admin_domain\",\n",
" \"insider_threat_indicator_score\": 0.0,\n",
" # Session aggregates (computed via compute_session_aggregates_for_user)\n",
" \"avg_session_duration_seconds\": 352.24,\n",
" \"avg_mfa_response_latency_ms\": 26.67,\n",
" \"avg_geo_anomaly_score\": 0.6474,\n",
" \"max_geo_anomaly_score\": 1.0,\n",
" \"frac_impossible_travel\": 0.36,\n",
" \"n_unique_countries\": 6,\n",
" \"n_unique_devices\": 25,\n",
" \"n_unique_applications\": 1,\n",
"}\n",
"\n",
"result = predict_risk_tier(example_record)\n",
"\n",
"print(f\"XGBoost -> {result['xgboost']['label']}\")\n",
"for lbl, p in sorted(result['xgboost']['probabilities'].items(), key=lambda x: -x[1]):\n",
" print(f\" P({lbl:8s}) = {p:.4f}\")\n",
"\n",
"print(f\"\\nMLP -> {result['mlp']['label']}\")\n",
"for lbl, p in sorted(result['mlp']['probabilities'].items(), key=lambda x: -x[1]):\n",
" print(f\" P({lbl:8s}) = {p:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Batch prediction on the sample dataset\n",
"\n",
"Score every user in `user_risk_summary.csv` after joining their session aggregates from `login_sessions.csv`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import snapshot_download\n",
"import pandas as pd\n",
"\n",
"ds_path = snapshot_download(repo_id=\"xpertsystems/cyb006-sample\", repo_type=\"dataset\")\n",
"users = pd.read_csv(f\"{ds_path}/user_risk_summary.csv\")\n",
"sessions = pd.read_csv(f\"{ds_path}/login_sessions.csv\")\n",
"\n",
"preds = []\n",
"for _, row in users.head(50).iterrows():\n",
" user_sessions = sessions[sessions[\"user_id\"] == row[\"user_id\"]]\n",
" if len(user_sessions) == 0:\n",
" continue\n",
" rec = row.to_dict()\n",
" rec.update(compute_session_aggregates_for_user(user_sessions))\n",
" pred = predict_risk_tier(rec)\n",
" preds.append({\n",
" \"user_id\": row[\"user_id\"],\n",
" \"true_tier\": row[\"user_risk_tier\"],\n",
" \"xgb_pred\": pred[\"xgboost\"][\"label\"],\n",
" })\n",
"\n",
"results = pd.DataFrame(preds)\n",
"ct = pd.crosstab(results[\"true_tier\"], results[\"xgb_pred\"],\n",
" rownames=[\"true\"], colnames=[\"pred\"])\n",
"print(\"Confusion on first 50 users (XGBoost):\")\n",
"print(ct)\n",
"acc = (results[\"true_tier\"] == results[\"xgb_pred\"]).mean()\n",
"print(f\"\\nbatch accuracy on first 50 users (in-distribution): {acc:.4f}\")\n",
"print(\"\\nNote: this includes training-set users. See validation_results.json\\n\"\n",
" \"for proper held-out test metrics.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Important: the leakage diagnostic\n",
"\n",
"Before using CYB006 sample data to train a threat-actor detector, read **`leakage_diagnostic.json`** in this repo. The README's stated headline use case (4-class threat-actor tier attribution) is not a representative ML task on the sample dataset — the synthetic generator produces threat-actor sessions with non-overlapping anomaly score distributions, so a plain XGBoost achieves 100% accuracy that doesn't reflect any real learning. The diagnostic documents which feature groups carry the leakage and what we recommend to dataset authors.\n",
"\n",
"This baseline ships `user_risk_tier` prediction instead, which has overlapping per-tier distributions and lifts ~10pp over majority baseline."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Next steps\n",
"\n",
"- See `validation_results.json` for held-out test metrics (30 disjoint users).\n",
"- See `multi_seed_results.json` for the across-10-seeds picture (accuracy 0.700 ± 0.082, ROC-AUC 0.812 ± 0.048).\n",
"- See `ablation_results.json` for per-feature-group contribution. User aggregate counts (failed logins, lateral hops, etc.) carry the most signal.\n",
"- See **`leakage_diagnostic.json`** for the detailed audit on threat-actor detection.\n",
"- For the full ~1.1M-row CYB006 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
]
}
],
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