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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CYB008 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 **SOC alert triage outcome** from a per-alert record.\n",
"\n",
"**Models predict one of 5 outcome classes:** `auto_resolved_soar`, `duplicate_merged`, `false_positive_closed`, `true_positive_remediated`, `true_positive_escalated`.\n",
"\n",
"**This is a baseline reference model**, not a production SOC triage system. See the model card and **especially `leakage_diagnostic.json`** for the structural-leakage findings (three columns were dropped as oracles)."
]
},
{
"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/cyb008-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())}\")\n",
"print(f\"\\noracle columns excluded (do not pass these to the model):\")\n",
"for c in meta.get(\"oracle_excluded\", []):\n",
" print(f\" - {c}\")"
]
},
{
"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 TriageMLP(nn.Module):\n",
" def __init__(self, n_features, n_classes=5, 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 = TriageMLP(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_triage_outcome(record: dict) -> dict:\n",
" \"\"\"Predict the resolution outcome for one SOC alert record.\n",
"\n",
" Note: do NOT include alert_lifecycle_phase, automation_resolved,\n",
" or escalation_flag in the record. These were structural oracles\n",
" in the training data and are excluded from the feature set.\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 high-severity ITDR identity-anomaly alert assigned to an L3 threat hunter, who escalated it to a true-positive incident. Both models should predict `true_positive_escalated` or the adjacent `true_positive_remediated`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Real alert from the sample dataset (true outcome: true_positive_escalated)\n",
"example_record = {\n",
" \"alert_severity\": \"high_severity\",\n",
" \"alert_source\": \"itdr_identity_anomaly\",\n",
" \"mitre_tactic\": \"initial_access\",\n",
" \"analyst_tier\": \"L3_threat_hunter\",\n",
" \"siem_platform\": \"logrhythm_axon\",\n",
" \"raw_score\": 0.2683,\n",
" \"enriched_score\": 0.343,\n",
" \"time_in_phase_minutes\": 429.26,\n",
" \"queue_depth_at_ingestion\": 0,\n",
" \"soar_playbook_triggered\": 0,\n",
" \"sla_breached_flag\": 1,\n",
" \"mttd_minutes\": 177.47,\n",
" \"mttr_minutes\": 429.26,\n",
" \"fatigue_score_at_alert\": 0.3805,\n",
"}\n",
"\n",
"result = predict_triage_outcome(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: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]):\n",
" print(f\" P({lbl:30s}) = {p:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Honest confusion between TP-remediated and TP-escalated\n",
"\n",
"The two `true_positive_*` outcomes look behaviourally similar in the data — both involve genuine threats. They differ by whether the alert was closed by the original analyst (remediated) or passed to a higher tier (escalated). When the trained models confuse these two classes on individual alerts, that's honest learning — not a defect.\n",
"\n",
"In a production triage workflow, the better operational metric is **TP vs FP** (recall on true positives, regardless of remediated/escalated). The published baseline achieves ROC-AUC 0.955 on the full 5-class task, which substantially exceeds practical thresholds for downstream binary TP-vs-FP decisions."
]
},
{
"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/cyb008-sample\", repo_type=\"dataset\")\n",
"alerts = pd.read_csv(f\"{ds_path}/soc_alerts.csv\")\n",
"\n",
"# Score the first 500 alerts\n",
"sample = alerts.head(500).copy()\n",
"preds = [predict_triage_outcome(row.to_dict())[\"xgboost\"][\"label\"] for _, row in sample.iterrows()]\n",
"sample[\"xgb_pred\"] = preds\n",
"\n",
"ct = pd.crosstab(sample[\"resolution_outcome\"], sample[\"xgb_pred\"],\n",
" rownames=[\"true\"], colnames=[\"pred\"])\n",
"print(\"Confusion on first 500 sample alerts (XGBoost):\")\n",
"print(ct)\n",
"acc = (sample[\"resolution_outcome\"] == sample[\"xgb_pred\"]).mean()\n",
"print(f\"\\nbatch accuracy on first 500 alerts (in-distribution): {acc:.4f}\")\n",
"print(\"\\nNote: this includes training-set alerts. See validation_results.json\\n\"\n",
" \"for proper held-out test metrics.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Important reading: the leakage diagnostic\n",
"\n",
"Before using CYB008 sample data to train your own triage model, read **`leakage_diagnostic.json`** in this repo. The CYB008 sample has three columns (`alert_lifecycle_phase`, `automation_resolved`, `escalation_flag`) that structurally encode the resolution_outcome label. With these columns present, a plain XGBoost achieves 100% accuracy that does not reflect real learning. The published baseline excludes them; the diagnostic file shows the cumulative ablation.\n",
"\n",
"The diagnostic also documents that **mitre_tactic prediction is unlearnable on this sample** (acc 0.08 vs majority 0.14). The README lists this as a top suggested use case, but the per-tactic feature distributions are too similar to learn from."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Next steps\n",
"\n",
"- See `validation_results.json` for held-out test metrics (1,380 alerts).\n",
"- See `multi_seed_results.json` for the across-10-seeds picture (accuracy 0.777 ± 0.007, ROC-AUC 0.955 ± 0.003).\n",
"- See `ablation_results.json` for per-feature-group contribution. Alert severity carries the dominant signal (−25 pp accuracy when removed); the SOAR-playbook-triggered indicator is second (−15 pp).\n",
"- See **`leakage_diagnostic.json`** for the full structural-leakage and unlearnable-target audit.\n",
"- For the full ~335k-row CYB008 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
]
}
],
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