README.md

---
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<n<10K
---

# 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**](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)