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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CYB005 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 **threat-actor capability tier** of a ransomware campaign from a per-timestep telemetry record.\n",
"\n",
"**Models predict one of 4 tiers:** `lone_actor`, `organised_syndicate`, `raas_affiliate`, `nation_state_nexus`.\n",
"\n",
"**This is a baseline reference model**, not a production threat-attribution 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/cyb005-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, build_segment_lookup\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": [
"# 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=4, 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. Build the segment lookup\n",
"\n",
"Per-segment topology features (SOC maturity, EDR coverage, backup tier, etc.) are pulled from `victim_topology.csv` and merged into each timestep record by `target_segment_id`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import snapshot_download\n",
"\n",
"ds_path = snapshot_download(repo_id=\"xpertsystems/cyb005-sample\", repo_type=\"dataset\")\n",
"\n",
"seg_lookup = build_segment_lookup(\n",
" os.path.join(ds_path, \"victim_topology.csv\")\n",
")\n",
"print(f\"loaded {len(seg_lookup)} segment profiles\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. 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_tier(record: dict) -> dict:\n",
" \"\"\"Predict the threat-actor tier for one per-timestep telemetry record.\n",
"\n",
" Per-segment topology features are pulled automatically via\n",
" `target_segment_id` from the seg_lookup loaded above.\n",
" \"\"\"\n",
" seg_id = record.get(\"target_segment_id\")\n",
" seg_aggs = seg_lookup.get(seg_id, {})\n",
" X = transform_single(record, meta, segment_aggregates=seg_aggs)\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": [
"## 6. Run on an example record\n",
"\n",
"Real `encryption_detonation` event from the sample dataset: a nation-state-tier ransomware campaign at timestep 68, with a wiper component deployed and 36,586 files encrypted across 634 endpoints. Both models should lean toward `nation_state_nexus`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Real timestep record from the sample dataset (true tier: nation_state_nexus)\n",
"example_record = {\n",
" \"timestep\": 68,\n",
" \"attack_phase\": \"encryption_detonation\",\n",
" \"files_encrypted_cumulative\": 36586,\n",
" \"encryption_throughput_mbps\": 244.913,\n",
" \"endpoints_compromised\": 634,\n",
" \"lateral_move_count\": 1498,\n",
" \"credential_harvest_count\": 17,\n",
" \"c2_bytes_exfiltrated\": 138747511.1,\n",
" \"defender_alert_score\": 1.0,\n",
" \"detection_outcome\": \"alert_generated\",\n",
" \"blast_radius_pct\": 0.4032,\n",
" \"living_off_land_score\": 0.35,\n",
" \"attribution_risk_score\": 0.0,\n",
" \"data_exfiltrated_gb\": 14.852,\n",
" \"wiper_flag\": 1,\n",
" \"double_extortion_flag\": 0,\n",
" \"ir_activated\": 0,\n",
" \"target_segment_id\": \"SEG00150\",\n",
"}\n",
"\n",
"result = predict_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: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 — `lone_actor` ↔ `nation_state_nexus` (low blast radius can look similar across both extremes), or `raas_affiliate` ↔ `organised_syndicate` (operational similarity). In threat-attribution workflows, disagreement is a useful triage signal for human analyst review."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Batch prediction on the sample dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"timelines = pd.read_csv(f\"{ds_path}/attack_timelines.csv\")\n",
"\n",
"# Score the first 500 timesteps\n",
"sample = timelines.head(500).copy()\n",
"preds = [predict_tier(row.to_dict())[\"xgboost\"][\"label\"] for _, row in sample.iterrows()]\n",
"sample[\"xgb_pred\"] = preds\n",
"\n",
"ct = pd.crosstab(sample[\"actor_capability_tier\"], sample[\"xgb_pred\"],\n",
" rownames=[\"true\"], colnames=[\"pred\"])\n",
"print(\"Confusion on first 500 sample rows (XGBoost):\")\n",
"print(ct)\n",
"acc = (sample[\"actor_capability_tier\"] == 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 campaigns. See validation_results.json\\n\"\n",
" \"for proper held-out test metrics from disjoint campaigns.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Next steps\n",
"\n",
"- See `validation_results.json` for held-out test metrics (75 disjoint campaigns, ~5,600 timesteps).\n",
"- See `multi_seed_results.json` for the across-10-seeds robustness picture (accuracy 0.603 ± 0.040, ROC-AUC 0.853 ± 0.031).\n",
"- See `ablation_results.json` for per-feature-group contribution. Behavioural features carry the most tier signal (−12pp accuracy when removed).\n",
"- The model card explains the leakage audit and the per-class tier-confusion patterns.\n",
"- For the full ~358k-row CYB005 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
]
}
],
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