{
 "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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