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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CYB007 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 **insider threat type** of an incident from a per-timestep trajectory record.\n",
"\n",
"**Models predict one of 3 tiers:** `negligent_user`, `malicious_employee`, `privileged_insider`.\n",
"\n",
"**This is a baseline reference model**, not a production insider-threat detection system. See the model card for full metrics and limitations."
]
},
{
"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/cyb007-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 transform_single, load_meta, INT_TO_LABEL"
]
},
{
"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": [
"# XGBoost\n",
"xgb_model = xgb.XGBClassifier()\n",
"xgb_model.load_model(files[\"model_xgb.json\"])\n",
"\n",
"# MLP architecture (must match training)\n",
"class TierMLP(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 = TierMLP(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_threat_type(record: dict) -> dict:\n",
" \"\"\"Predict the actor threat type for one per-timestep telemetry record.\"\"\"\n",
" X = transform_single(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 `exfiltration_attempt` event from the sample dataset: a privileged-insider incident at timestep 31, accessing 424 MB at a single step with internal-tier data and a moderate-risk DLP alert. Both models should predict `privileged_insider` (large per-step data volume is a strong privileged-insider signature)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Real timestep record from the sample dataset (true tier: privileged_insider)\n",
"example_record = {\n",
" \"timestep\": 31,\n",
" \"incident_phase\": \"exfiltration_attempt\",\n",
" \"data_access_volume_mb\": 424.4688,\n",
" \"privilege_event_count\": 2,\n",
" \"communication_anomaly_score\": 0.407904,\n",
" \"dlp_confidence_score\": 0.652392,\n",
" \"detection_outcome\": \"moderate_risk_alert\",\n",
" \"exfiltration_volume_mb_cumulative\": 0.0,\n",
" \"behavioural_risk_score\": 0.301542,\n",
" \"target_data_sensitivity_tier\": \"internal\",\n",
"}\n",
"\n",
"result = predict_threat_type(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:25s}) = {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:25s}) = {p:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### When the two models disagree\n",
"\n",
"XGBoost and the MLP can disagree on borderline cases — e.g. low-volume timesteps where a malicious employee might look similar to a negligent user, or early-stage timesteps before tier-distinguishing behaviour appears. In threat-investigation workflows, disagreement is a useful triage signal for human analyst review.\n",
"\n",
"Unusually for the XpertSystems baseline catalog, on CYB007 the **MLP slightly outperforms XGBoost** at multi-seed evaluation (acc 0.869 vs 0.853 at seed 42). Both are published; we recommend running both and treating disagreement as the triage signal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Batch prediction on the sample dataset"
]
},
{
"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/cyb007-sample\", repo_type=\"dataset\")\n",
"traj = pd.read_csv(f\"{ds_path}/insider_trajectories.csv\")\n",
"\n",
"# Score the first 500 timesteps\n",
"sample = traj.head(500).copy()\n",
"preds = [predict_threat_type(row.to_dict())[\"xgboost\"][\"label\"] for _, row in sample.iterrows()]\n",
"sample[\"xgb_pred\"] = preds\n",
"\n",
"ct = pd.crosstab(sample[\"actor_threat_type\"], sample[\"xgb_pred\"],\n",
" rownames=[\"true\"], colnames=[\"pred\"])\n",
"print(\"Confusion on first 500 sample rows (XGBoost):\")\n",
"print(ct)\n",
"acc = (sample[\"actor_threat_type\"] == sample[\"xgb_pred\"]).mean()\n",
"print(f\"\\nbatch accuracy on first 500 rows (in-distribution): {acc:.4f}\")\n",
"print(\"\\nNote: these rows include training-set incidents. See validation_results.json\\n\"\n",
" \"for proper held-out test metrics from disjoint incidents.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Next steps\n",
"\n",
"- See `validation_results.json` for held-out test metrics (75 disjoint incidents, ~4,875 timesteps).\n",
"- See `multi_seed_results.json` for the across-10-seeds robustness picture (accuracy 0.855 ± 0.012, ROC-AUC 0.961 ± 0.007).\n",
"- See `ablation_results.json` for per-feature-group contribution. **Volume features carry the dominant tier signal** (−36pp accuracy when removed) — this is the defining behavioural signature of privileged_insider tier.\n",
"- The model card documents the leakage audit on volume features (they are tier-correlated by design but have substantial distributional overlap — not oracles).\n",
"- For the full ~335k-row CYB007 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
]
}
],
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