{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CYB011 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 **adversarial attack phase** for a per-timestep trajectory record.\n",
    "\n",
    "**Models predict one of 7 phases:** `reconnaissance`, `feature_space_probe`, `perturbation_craft`, `evasion_attempt`, `feedback_adaptation`, `campaign_consolidation`, `idle_dwell`.\n",
    "\n",
    "**This is a baseline reference model**, not a production phase classifier. See the model card and **`leakage_diagnostic.json`** for the structural-leakage findings (6 oracle paths documented across the dataset, 4 README-suggested targets unlearnable after honest leak removal)."
   ]
  },
  {
   "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/cyb011-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, build_segment_lookup, INT_TO_LABEL,\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())}\")\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 PhaseMLP(nn.Module):\n",
    "    def __init__(self, n_features, n_classes=7, 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. Load segment topology for defender-feature lookup\n",
    "\n",
    "The model uses segment context (defender_architecture, detection_strength, ensemble_size, etc.) as features. To predict on a new trajectory, we look up its segment features from the network_topology."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import snapshot_download\n",
    "\n",
    "ds_path = snapshot_download(repo_id=\"xpertsystems/cyb011-sample\", repo_type=\"dataset\")\n",
    "segment_lookup = build_segment_lookup(f\"{ds_path}/network_topology.csv\")\n",
    "print(f\"loaded {len(segment_lookup)} segment records\")"
   ]
  },
  {
   "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_attack_phase(record: dict) -> dict:\n",
    "    \"\"\"Predict the adversarial attack phase for one trajectory record.\n",
    "\n",
    "    Note: do NOT include detection_outcome, detector_confidence_score,\n",
    "    or evasion_budget_consumed in the record. These were outcome leaks\n",
    "    in the training data and are excluded from the feature set.\n",
    "\n",
    "    Segment features (defender_architecture, detection_strength, etc.)\n",
    "    are looked up from network_topology by target_segment_id.\n",
    "    \"\"\"\n",
    "    X = transform_single(record, meta, segment_lookup=segment_lookup)\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 APT-tier trajectory at timestep 21 (mid-campaign). True phase is `evasion_attempt` — the attacker has built up 11 queries and is actively perturbing inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Real trajectory record from the sample dataset (true phase: evasion_attempt)\n",
    "# Note: target_segment_id is supplied so segment features are auto-looked-up\n",
    "example_record = {\n",
    "    \"target_segment_id\": \"SEG00197\",\n",
    "    \"timestep\": 21,\n",
    "    \"perturbation_magnitude\": 0.14152,\n",
    "    \"feature_delta_l2_norm\": 1.278436,\n",
    "    \"feature_delta_linf_norm\": 0.14152,\n",
    "    \"query_count_cumulative\": 11,\n",
    "    \"attacker_capability_tier\": \"advanced_persistent_threat\",\n",
    "}\n",
    "\n",
    "result = predict_attack_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]):\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": [
    "### Per-class confidence patterns\n",
    "\n",
    "The model has strong confidence on `evasion_attempt` (per-class F1 1.00), `reconnaissance` (F1 0.89), and `campaign_consolidation` (F1 0.81) — these phases have distinctive feature signatures (query usage, timestep position, perturbation activity).\n",
    "\n",
    "The middle phases overlap more in feature space. `perturbation_craft` is the hardest class (F1 0.49) because its trajectory features look similar to `feature_space_probe` at the per-timestep level. A sequence model considering event ordering within campaigns would likely do better than per-timestep classification."
   ]
  },
  {
   "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",
    "trajectories = pd.read_csv(f\"{ds_path}/attack_trajectories.csv\")\n",
    "\n",
    "# Score the first 500 events\n",
    "sample = trajectories.head(500).copy()\n",
    "preds = [predict_attack_phase(row.to_dict())[\"xgboost\"][\"label\"] for _, row in sample.iterrows()]\n",
    "sample[\"xgb_pred\"] = preds\n",
    "\n",
    "ct = pd.crosstab(sample[\"attack_phase\"], sample[\"xgb_pred\"],\n",
    "                 rownames=[\"true\"], colnames=[\"pred\"])\n",
    "print(\"Confusion on first 500 sample events (XGBoost):\")\n",
    "print(ct)\n",
    "acc = (sample[\"attack_phase\"] == sample[\"xgb_pred\"]).mean()\n",
    "print(f\"\\nbatch accuracy on first 500 events (in-distribution): {acc:.4f}\")\n",
    "print(\"\\nNote: this includes training-set events. See validation_results.json\\n\"\n",
    "      \"for proper held-out test metrics (group-aware split by campaign_id).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Important reading: the leakage diagnostic\n",
    "\n",
    "Before using CYB011 sample data to train your own models, read **`leakage_diagnostic.json`** in this repo. It documents **6 oracle paths** across the sample's targets:\n",
    "\n",
    "**Phase target oracles (3 paths — dropped from features):**\n",
    "1. `detection_outcome` (`!= suppressed_alert` → 100% `evasion_attempt`)\n",
    "2. `detector_confidence_score` (threshold-derived from `detection_outcome`)\n",
    "3. `evasion_budget_consumed` (`== 0` → 100% one of 3 early phases)\n",
    "\n",
    "**Other documented leaks (for transparency, not features for this model):**\n",
    "4. `stealth_score` near-deterministic per `attacker_capability_tier` (campaign-level)\n",
    "5. Topology fingerprint (7 segment-level features uniquely identify `defender_architecture`)\n",
    "6. `timestep` partial oracle for 3 phases — **KEPT as legitimate campaign-progress observable**\n",
    "\n",
    "It also documents **4 README-suggested headline targets that are unlearnable on the sample** after honest leak removal: `campaign_success_flag`, `campaign_type` 8-class, `coordinated_attack_flag`, `defender_architecture` 8-class.\n",
    "\n",
    "And it documents the **missing `nation_state` attacker tier** — README claims 4 tiers, sample contains only 3 (script_kiddie, opportunistic, APT)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Next steps\n",
    "\n",
    "- See `validation_results.json` for held-out test metrics (2,100 events from ~30 test campaigns).\n",
    "- See `multi_seed_results.json` for the across-10-seeds picture (accuracy 0.867 ± 0.010, ROC-AUC 0.977 ± 0.002).\n",
    "- See `ablation_results.json` for per-feature-group contribution. Perturbation features carry the most signal (−20pp accuracy when removed); query features second (−4pp).\n",
    "- See **`leakage_diagnostic.json`** for the full 6-oracle-path audit and 4 unlearnable targets.\n",
    "- For the full ~383k-row CYB011 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}