--- license: cc-by-nc-4.0 task_categories: - tabular-classification - time-series-forecasting tags: - cybersecurity - mitre-attack - kill-chain - apt - ransomware - synthetic-data - threat-modeling - red-team - blue-team pretty_name: CYB002 — Synthetic Cyber Attack Dataset (Sample) size_categories: - 1K 🤖 **Trained baseline available:** > [**xpertsystems/cyb002-baseline-classifier**](https://huggingface.co/xpertsystems/cyb002-baseline-classifier) > — XGBoost + PyTorch MLP for 10-class MITRE ATT&CK kill-chain phase > prediction, group-aware split by campaign, ablation evidence, > honest limitations in the model card. | File | Rows (sample) | Rows (full) | Description | |-------------------------|---------------|---------------|----------------------------------------------| | `network_topology.csv` | ~651 | ~3,200 | Network segments and asset inventory | | `campaign_summary.csv` | ~100 | ~6,000 | Per-campaign outcome aggregates | | `campaign_events.csv` | ~739 | ~65,000 | Discrete campaign event log | | `attack_events.csv` | ~4,353 | ~380,000 | Timestep-level kill-chain events | ## Dataset Summary CYB002 simulates end-to-end cyber attack lifecycles as a **9-phase MITRE ATT&CK kill-chain state machine** across enterprise, cloud, endpoint, and OT/ICS environments, with: - **9 ATT&CK phases**: reconnaissance, resource_development, initial_access, execution, persistence, privilege_escalation, defense_evasion, lateral_movement, exfiltration - **4 attacker capability tiers**: opportunistic, organized_crime, apt, nation_state — with per-tier dwell time, lateral hop rate, and stealth weight distributions - **5 defender maturity levels**: ad_hoc, defined, managed, quantitatively_ managed, optimizing - **MITRE ATT&CK technique catalogue** with representative subset of Enterprise v14 techniques mapped to each phase - **EDR coverage modelling** with configurable effectiveness - **Ransomware deployment, supply chain compromise, and exfiltration** outcome paths ## Trained Baseline Available A working baseline classifier trained on this sample is published at **[xpertsystems/cyb002-baseline-classifier](https://huggingface.co/xpertsystems/cyb002-baseline-classifier)**. | Component | Detail | |---|---| | Task | 10-class MITRE ATT&CK kill-chain phase classification | | Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) | | Features | 90 (after one-hot encoding); pipeline included as `feature_engineering.py` | | Split | **Group-aware by campaign_id** — train/val/test campaigns disjoint | | Demo | `inference_example.ipynb` — end-to-end copy-paste | | Headline metrics | XGBoost macro ROC-AUC 0.86; accuracy 47% (vs 19% always-majority baseline) | The model card documents the three columns excluded for label leakage (`technique_id`, `technique_name`, `tactic_category`), an ablation showing `timestep` carries most of the phase signal, and six explicit limitations including the gap between synthetic and real attack telemetry. Late-stage phases (collection / exfiltration / impact) are genuinely harder for a flat-tabular event-level model — the baseline exposes this honestly. ## Calibrated Benchmark Targets The full product is calibrated to 12 benchmark metrics drawn from authoritative threat intelligence sources (Mandiant M-Trends, IBM CODB, Verizon DBIR, CrowdStrike GTR, MITRE ATT&CK Evaluations, SANS, ENISA). The sample preserves the same calibration. Observed values on this sample: | Test | Target | Observed | Verdict | |------|--------|----------|---------| | dwell_time_hours_apt | 21.0000 | 21.1595 | ✓ PASS | | detection_rate_advanced | 0.8600 | 0.8600 | ✓ PASS | | exfiltration_success_rate | 0.3100 | 0.3000 | ✓ PASS | | lateral_hop_rate_apt | 0.0720 | 0.0552 | ✓ PASS | | suppressed_alert_rate | 0.0770 | 0.0719 | ✓ PASS | | mttd_hours_advanced | 4.2000 | 3.3541 | ✓ PASS | | mttr_hours_advanced | 18.0000 | 19.7415 | ✓ PASS | | ransomware_deployment_rate | 0.2400 | 0.2100 | ✓ PASS | | campaign_success_rate | 0.3400 | 0.4300 | ~ MARGINAL | | privilege_escalation_rate | 0.6200 | 0.6600 | ✓ PASS | | edr_block_rate | 0.4300 | 0.3680 | ~ MARGINAL | | supply_chain_compromise_rate | 0.0850 | 0.0800 | ✓ PASS | *Note: some benchmarks (e.g. APT dwell time, MTTR) require larger sample sizes to converge. The full product passes all 12 benchmarks at Grade A-.* ## Schema Highlights ### `attack_events.csv` (primary file, timestep-level) | Column | Type | Description | |------------------------------|---------|----------------------------------------------| | campaign_id | string | Parent campaign FK | | attacker_id | string | Attacker FK | | timestep | int | Step in kill-chain simulation | | phase | string | 1 of 9 ATT&CK phases | | technique_id | string | MITRE ATT&CK technique ID (e.g. T1059.001) | | technique_name | string | Human-readable technique name | | tactic | string | ATT&CK tactic category | | segment_id | string | FK to `network_topology.csv` | | asset_id | string | Target asset within segment | | attacker_tier | string | opportunistic / organized_crime / apt / nation_state | | defender_maturity | string | ad_hoc / defined / managed / quant / optimizing | | stealth_score | float | Action stealth weight (0–1) | | detected | int | Whether action was detected (0/1) | | blocked | int | Whether action was blocked (0/1) | | edr_deployed | int | EDR present on target asset | | alert_severity | string | INFO / LOW / MEDIUM / HIGH / CRITICAL | | dwell_hours_so_far | float | Cumulative dwell time at this step | ### `campaign_summary.csv` (per-campaign outcome) | Column | Type | Description | |---------------------------------|---------|--------------------------------------------| | campaign_id, attacker_id | string | Identifiers | | attacker_tier, defender_maturity| string | Categorical | | campaign_outcome | string | success / detected / blocked / aborted | | total_dwell_hours | float | End-to-end attacker dwell time | | mttd_hours, mttr_hours | float | Mean time to detect / respond | | exfiltrated_bytes | int | Bytes exfiltrated (0 if none) | | ransomware_deployed | int | Boolean | | lateral_hops | int | Count of lateral movement actions | | privilege_escalated | int | Boolean | | supply_chain_used | int | Boolean | See `campaign_events.csv` and `network_topology.csv` for the discrete event log and asset inventory schemas respectively. ## Suggested Use Cases - Training **kill-chain phase classifiers** (predict next ATT&CK phase) — [worked example available](https://huggingface.co/xpertsystems/cyb002-baseline-classifier) - Benchmarking **APT detection** algorithms (long dwell, low stealth_score) - **Campaign outcome prediction** (success / detected / blocked / aborted) - **MTTD / MTTR forecasting** under varying defender maturity - **Ransomware risk modelling** across attacker tiers - **Red-team simulation training data** for purple-team exercises - **SOC alert triage** benchmarking with realistic severity distributions ## Loading the Data ```python import pandas as pd attacks = pd.read_csv("attack_events.csv") campaigns = pd.read_csv("campaign_summary.csv") events = pd.read_csv("campaign_events.csv") topology = pd.read_csv("network_topology.csv") # Join to get the full attack context enriched = attacks.merge(campaigns, on=["campaign_id", "attacker_id"], how="left") enriched = enriched.merge(topology, on="segment_id", how="left") # Binary detection target y = attacks["detected"].astype(int) # Campaign-level outcome target y_outcome = campaigns["campaign_outcome"] ``` For a worked end-to-end example with the 10-class kill-chain phase target, group-aware splitting, and feature engineering, see the inference notebook in the [baseline classifier repo](https://huggingface.co/xpertsystems/cyb002-baseline-classifier/blob/main/inference_example.ipynb). ## License This **sample** is released under **CC-BY-NC-4.0** (free for non-commercial research and evaluation). The **full production dataset** is licensed commercially — contact XpertSystems.ai for licensing terms. ## Full Product The full CYB002 dataset includes **~454,000 rows** across all four files, with calibrated benchmark validation against 12 metrics drawn from authoritative threat intelligence sources. 📧 **pradeep@xpertsystems.ai** 🌐 **https://xpertsystems.ai** ## Citation ```bibtex @dataset{xpertsystems_cyb002_sample_2026, title = {CYB002: Synthetic Cyber Attack Dataset (Sample)}, author = {XpertSystems.ai}, year = {2026}, url = {https://huggingface.co/datasets/xpertsystems/cyb002-sample} } ``` ## Generation Details - Generator version : 2.0.0 - Random seed : 42 - Generated : 2026-05-16 13:39:22 UTC - Kill-chain model : 9-phase MITRE ATT&CK state machine - Overall benchmark : 95.3 / 100 (grade A)