xpertsystems/cyb002-sample

CYB002 — Synthetic Cyber Attack Dataset (Sample)

XpertSystems.ai Synthetic Data Platform · SKU: CYB002-SAMPLE · Version 1.0.0

This is a free preview of the full CYB002 — Synthetic Cyber Attack Dataset product. It contains roughly 1 / 60th of the full dataset at identical schema, attacker-tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

🤖 Trained baseline available: 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.

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
• 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

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.

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

@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)