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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CYB001 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 on a new flow record.\n",
"\n",
"**Models predict one of three labels:** `BENIGN`, `MALICIOUS`, or `AMBIGUOUS`.\n",
"\n",
"**This is a baseline reference model**, not a production IDS. See the model card for full 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 the one used at training)\n",
"- `feature_meta.json` — feature column order + categorical levels\n",
"- `feature_scaler.json` — MLP input standardization (mean / std)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import hf_hub_download\n",
"\n",
"REPO_ID = \"xpertsystems/cyb001-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": [
"# Make feature_engineering.py importable\n",
"import sys, shutil, 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",
"# --- Metadata ---\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",
"print(f\"feature count: {N_FEATURES}\")\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 FlowMLP(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 = FlowMLP(N_FEATURES)\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. Define a prediction function"
]
},
{
"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_flow(record: dict) -> dict:\n",
" \"\"\"\n",
" Predict the label for one flow record. `record` is a dict containing\n",
" the fields described in the model card's 'Input schema' section.\n",
"\n",
" Returns a dict with both models' predictions and per-class probabilities.\n",
" \"\"\"\n",
" X = transform_single(record, meta)\n",
"\n",
" # XGBoost\n",
" xgb_proba = xgb_model.predict_proba(X)[0]\n",
" xgb_label = INT_TO_LABEL[int(np.argmax(xgb_proba))]\n",
"\n",
" # MLP\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",
"The fields below are the union of `network_flows.csv`, the joined session-summary subset, and the joined topology fields. In a real deployment you would assemble these by joining a new flow against your session-summary store and your topology lookup.\n",
"\n",
"This example is a real `BENIGN` HTTPS flow lifted from the sample dataset (workstation → cloud service, port 443). Both models should agree."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# A real BENIGN HTTPS flow from the sample dataset.\n",
"# Workstation -> cloud service, port 443, mid-day. Both models should\n",
"# agree on BENIGN. If you hand-construct records, expect occasional\n",
"# disagreement between XGBoost and MLP on out-of-distribution inputs -\n",
"# disagreement is itself a useful signal; see note below.\n",
"example_record = {\n",
" # ---- flow-level fields ----\n",
" \"source_port\": 52789, \"dest_port\": 443, \"protocol\": \"HTTPS\",\n",
" \"flow_start_timestamp\": \"2024-01-20 13:27:58.967\",\n",
" \"flow_duration_ms\": 535,\n",
" \"total_fwd_packets\": 37, \"total_bwd_packets\": 30,\n",
" \"total_bytes_fwd\": 17020, \"total_bytes_bwd\": 23310,\n",
" \"fwd_packet_len_mean\": 460, \"fwd_packet_len_std\": 296,\n",
" \"bwd_packet_len_mean\": 777, \"bwd_packet_len_std\": 226,\n",
" \"flow_bytes_per_sec\": 75383.18, \"flow_packets_per_sec\": 125.23,\n",
" \"inter_arrival_time_mean\": 20.618, \"inter_arrival_time_std\": 8.457,\n",
" \"tcp_flag_syn_count\": 0, \"tcp_flag_ack_count\": 0, \"tcp_flag_fin_count\": 0,\n",
" \"tcp_flag_rst_count\": 0, \"tcp_flag_psh_count\": 0, \"tcp_flag_urg_count\": 0,\n",
" \"flow_lifecycle_phase\": \"protocol_handshake\",\n",
" \"source_device_type\": \"workstation\", \"dest_device_type\": \"cloud_service\",\n",
" \"retransmission_flag\": 0, \"fragmentation_flag\": 0, \"protocol_violation_flag\": 0,\n",
"\n",
" # ---- session-level fields (from session_summary.csv join) ----\n",
" \"payload_entropy_mean\": 3.6328,\n",
" \"retransmission_rate\": 0.0631,\n",
" \"protocol_violation_count\": 0,\n",
" \"c2_beacon_flag\": 0,\n",
" \"session_risk_score\": 0.1866,\n",
"\n",
" # ---- topology fields (from network_topology.csv join) ----\n",
" \"segment_type\": \"corporate_lan\",\n",
" \"trust_level\": 0.6027, \"avg_concurrent_flows\": 109, \"bandwidth_mbps\": 671.0,\n",
" \"nat_enabled\": 1, \"ids_coverage\": 0.8253, \"diurnal_peak_factor\": 1.6239,\n",
" \"feature_space_dim\": 107, \"alert_threshold\": 0.3089,\n",
" \"retraining_cadence_days\": 39, \"ensemble_size\": 1, \"device_count\": 302,\n",
" \"firewall_policy\": \"zone_based\", \"qos_policy\": \"best_effort\",\n",
" \"defender_architecture\": \"lstm_behavioural\",\n",
"}\n",
"\n",
"result = predict_flow(example_record)\n",
"\n",
"print(f\"XGBoost -> {result['xgboost']['label']}\")\n",
"for lbl, p in result['xgboost']['probabilities'].items():\n",
" print(f\" P({lbl}) = {p:.4f}\")\n",
"\n",
"print(f\"\\nMLP -> {result['mlp']['label']}\")\n",
"for lbl, p in result['mlp']['probabilities'].items():\n",
" print(f\" P({lbl}) = {p:.4f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Note: when the two models disagree\n",
"\n",
"XGBoost and the MLP can disagree on out-of-distribution records — particularly hand-crafted inputs whose feature combinations don't lie on the training-data manifold. The MLP, with BatchNorm and only ~7k training rows, has narrower competence than the tree ensemble. Disagreement is itself a useful triage signal: in a production pipeline you would surface those flows for human review rather than auto-act on either prediction.\n",
"\n",
"On in-distribution records (e.g. real rows from the sample CSV, as used in section 6 below) the two models agree on >99% of cases."
]
},
{
"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",
"# Pull the sample dataset CSVs\n",
"ds_path = snapshot_download(repo_id=\"xpertsystems/cyb001-sample\", repo_type=\"dataset\")\n",
"\n",
"flows = pd.read_csv(f\"{ds_path}/network_flows.csv\")\n",
"sessions = pd.read_csv(f\"{ds_path}/session_summary.csv\")\n",
"topology = pd.read_csv(f\"{ds_path}/network_topology.csv\")\n",
"\n",
"# Drop leaky columns the model was never trained on\n",
"flows = flows.drop(columns=[\"traffic_category\", \"attack_subcategory\",\n",
" \"attacker_capability_tier\"], errors=\"ignore\")\n",
"\n",
"# Build the same enriched frame the training pipeline used\n",
"enriched = flows.merge(\n",
" sessions[[\"session_id\", \"payload_entropy_mean\", \"retransmission_rate\",\n",
" \"protocol_violation_count\", \"c2_beacon_flag\", \"session_risk_score\"]],\n",
" on=\"session_id\", how=\"left\",\n",
").merge(topology, on=\"segment_id\", how=\"left\")\n",
"\n",
"# Score the first 200 rows\n",
"sample = enriched.head(200).copy()\n",
"preds = []\n",
"for _, row in sample.iterrows():\n",
" out = predict_flow(row.to_dict())\n",
" preds.append(out[\"xgboost\"][\"label\"])\n",
"\n",
"sample[\"xgb_pred\"] = preds\n",
"\n",
"# Confusion vs ground-truth label\n",
"ct = pd.crosstab(sample[\"label\"], sample[\"xgb_pred\"], rownames=[\"true\"], colnames=[\"pred\"])\n",
"print(\"Confusion on first 200 sample rows (XGBoost):\")\n",
"print(ct)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Next steps\n",
"\n",
"- See `validation_results.json` for full test-set metrics and architecture details.\n",
"- The high accuracy is a property of calibrated synthetic data — see the model card's **Limitations** section before extrapolating to production traffic.\n",
"- For the full 685k-row CYB001 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
]
}
],
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