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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CYB003 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 **malware execution phase** of a new per-timestep telemetry record.\n",
"\n",
"**Models predict one of 10 phases:** `c2_communication`, `data_exfiltration`, `dormancy_dwell`, `initial_drop`, `lateral_movement`, `payload_execution`, `persistence_establishment`, `privilege_escalation`, `sandbox_evasion_stall`, `self_destruct_cleanup`.\n",
"\n",
"**This is a baseline reference model**, not a production sandbox or EDR. 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\n",
"\n",
"Five files are needed:\n",
"- `model_xgb.json` — XGBoost weights\n",
"- `model_mlp.safetensors` — PyTorch MLP weights\n",
"- `feature_engineering.py` — feature pipeline (must match training)\n",
"- `feature_meta.json` — feature column order + categorical levels\n",
"- `feature_scaler.json` — MLP input standardization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import hf_hub_download\n",
"\n",
"REPO_ID = \"xpertsystems/cyb003-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 PhaseMLP(nn.Module):\n",
" def __init__(self, n_features, n_classes=10, 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 = PhaseMLP(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_phase(record: dict) -> dict:\n",
" \"\"\"Predict the execution phase for one per-timestep telemetry record.\n",
"\n",
" Returns a dict with both models' predictions and per-class probabilities.\n",
" \"\"\"\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 `lateral_movement` event lifted from the sample dataset: an APT-tier cryptominer at timestep 26 propagating laterally with 2 propagation events and 10 network connections. Both models should predict `lateral_movement`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Real timestep record from the sample dataset (true phase: lateral_movement)\n",
"example_record = {\n",
" \"timestep\": 26,\n",
" \"malware_family\": \"cryptominer\",\n",
" \"threat_actor_tier\": \"apt\",\n",
" \"target_platform\": \"windows_10_enterprise\",\n",
" \"obfuscation_technique\": \"code_signing_abuse\",\n",
" \"api_call_rate\": 1.4167,\n",
" \"registry_write_count\": 0,\n",
" \"network_connection_count\": 10,\n",
" \"process_injection_flag\": 1,\n",
" \"c2_beacon_interval_sec\": 0.0,\n",
" \"detection_outcome\": \"signature_miss\",\n",
" \"av_signature_hit_flag\": 0,\n",
" \"sandbox_evasion_flag\": 0,\n",
" \"lateral_propagation_count\": 2,\n",
" \"privilege_escalation_flag\": 0,\n",
" \"ep_stack\": \"deception_honeypot\",\n",
" \"pe_entropy_mean\": 0.8336,\n",
" \"pe_entropy_std\": 0.25,\n",
" \"import_hash_cluster\": 498,\n",
" \"section_count\": 2,\n",
" \"packed_section_ratio\": 0.7558,\n",
" \"string_entropy_mean\": 0.5727,\n",
" \"byte_histogram_chi2\": 45.52,\n",
" \"code_section_rx_ratio\": 0.3628,\n",
" \"resource_section_entropy\": 0.4418,\n",
" \"suspicious_import_count\": 11,\n",
" \"packer_detected_flag\": 1,\n",
"}\n",
"\n",
"result = predict_phase(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])[:5]:\n",
" print(f\" P({lbl:30s}) = {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])[:5]:\n",
" print(f\" P({lbl:30s}) = {p:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Note: when the two models disagree\n",
"\n",
"XGBoost and the MLP can disagree on records far from the training-data manifold or in the three phases the baseline finds genuinely hard (`dormancy_dwell`, `sandbox_evasion_stall`, `self_destruct_cleanup`, each spanning the full timestep range). Disagreement is a useful signal: hand those cases to a human analyst or to a more expensive sequence-based detector."
]
},
{
"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/cyb003-sample\", repo_type=\"dataset\")\n",
"samples = pd.read_csv(f\"{ds_path}/malware_samples.csv\")\n",
"\n",
"# Score the first 200 timesteps\n",
"sample = samples.head(200).copy()\n",
"preds = [predict_phase(row.to_dict())[\"xgboost\"][\"label\"] for _, row in sample.iterrows()]\n",
"sample[\"xgb_pred\"] = preds\n",
"\n",
"ct = pd.crosstab(sample[\"execution_phase\"], sample[\"xgb_pred\"],\n",
" rownames=[\"true\"], colnames=[\"pred\"])\n",
"print(\"Confusion on first 200 sample rows (XGBoost):\")\n",
"print(ct)\n",
"acc = (sample[\"execution_phase\"] == sample[\"xgb_pred\"]).mean()\n",
"print(f\"\\nbatch accuracy on first 200 rows (in-distribution): {acc:.4f}\")\n",
"print(\"\\nNote: these rows include training-set samples. See validation_results.json\\n\"\n",
" \"for proper held-out test metrics from disjoint samples.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Next steps\n",
"\n",
"- See `validation_results.json` for held-out test metrics (15 disjoint samples, 900 timesteps).\n",
"- See `multi_seed_results.json` for the across-10-seeds robustness picture (accuracy 0.905 ± 0.010).\n",
"- See `ablation_results.json` for per-feature-group contribution. `timestep` carries the dominant signal — kill chains progress in time, malware execution does too.\n",
"- The model card's **Limitations** section explains why `dormancy_dwell`, `sandbox_evasion_stall`, and `self_destruct_cleanup` are hard.\n",
"- For the full 280k-row CYB003 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
]
}
],
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