| ---
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| license: cc-by-nc-4.0
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| task_categories:
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| - tabular-classification
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| - time-series-forecasting
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| tags:
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| - cybersecurity
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| - mitre-attack
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| - kill-chain
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| - apt
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| - ransomware
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| - synthetic-data
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| - threat-modeling
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| - red-team
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| - blue-team
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| pretty_name: CYB002 — Synthetic Cyber Attack Dataset (Sample)
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| size_categories:
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| - 1K<n<10K
|
| ---
|
|
|
| # CYB002 — Synthetic Cyber Attack Dataset (Sample)
|
|
|
| **XpertSystems.ai Synthetic Data Platform · SKU: CYB002-SAMPLE · Version 1.0.0**
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|
|
| This is a **free preview** of the full **CYB002 — Synthetic Cyber Attack
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| Dataset** product. It contains roughly **1 / 60th of the full dataset** at
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| identical schema, attacker-tier distribution, and statistical fingerprint, so
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| you can evaluate fit before licensing the full product.
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|
|
| > 🤖 **Trained baseline available:**
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| > [**xpertsystems/cyb002-baseline-classifier**](https://huggingface.co/xpertsystems/cyb002-baseline-classifier)
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| > — XGBoost + PyTorch MLP for 10-class MITRE ATT&CK kill-chain phase
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| > prediction, group-aware split by campaign, ablation evidence,
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| > honest limitations in the model card.
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|
|
| | File | Rows (sample) | Rows (full) | Description |
|
| |-------------------------|---------------|---------------|----------------------------------------------|
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| | `network_topology.csv` | ~651 | ~3,200 | Network segments and asset inventory |
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| | `campaign_summary.csv` | ~100 | ~6,000 | Per-campaign outcome aggregates |
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| | `campaign_events.csv` | ~739 | ~65,000 | Discrete campaign event log |
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| | `attack_events.csv` | ~4,353 | ~380,000 | Timestep-level kill-chain events |
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|
|
| ## Dataset Summary
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|
|
| CYB002 simulates end-to-end cyber attack lifecycles as a **9-phase MITRE
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| ATT&CK kill-chain state machine** across enterprise, cloud, endpoint, and
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| OT/ICS environments, with:
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|
|
| - **9 ATT&CK phases**: reconnaissance, resource_development, initial_access,
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| execution, persistence, privilege_escalation, defense_evasion,
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| lateral_movement, exfiltration
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| - **4 attacker capability tiers**: opportunistic, organized_crime, apt,
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| nation_state — with per-tier dwell time, lateral hop rate, and stealth
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| weight distributions
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| - **5 defender maturity levels**: ad_hoc, defined, managed, quantitatively_
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| managed, optimizing
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| - **MITRE ATT&CK technique catalogue** with representative subset of
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| Enterprise v14 techniques mapped to each phase
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| - **EDR coverage modelling** with configurable effectiveness
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| - **Ransomware deployment, supply chain compromise, and exfiltration**
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| outcome paths
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|
|
| ## Trained Baseline Available
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|
|
| A working baseline classifier trained on this sample is published at
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| **[xpertsystems/cyb002-baseline-classifier](https://huggingface.co/xpertsystems/cyb002-baseline-classifier)**.
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|
|
| | Component | Detail |
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| |---|---|
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| | Task | 10-class MITRE ATT&CK kill-chain phase classification |
|
| | Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
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| | Features | 90 (after one-hot encoding); pipeline included as `feature_engineering.py` |
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| | Split | **Group-aware by campaign_id** — train/val/test campaigns disjoint |
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| | Demo | `inference_example.ipynb` — end-to-end copy-paste |
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| | Headline metrics | XGBoost macro ROC-AUC 0.86; accuracy 47% (vs 19% always-majority baseline) |
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|
|
| The model card documents the three columns excluded for label leakage
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| (`technique_id`, `technique_name`, `tactic_category`), an ablation
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| showing `timestep` carries most of the phase signal, and six explicit
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| limitations including the gap between synthetic and real attack
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| telemetry. Late-stage phases (collection / exfiltration / impact) are
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| genuinely harder for a flat-tabular event-level model — the baseline
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| exposes this honestly.
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|
|
| ## Calibrated Benchmark Targets
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|
|
| The full product is calibrated to 12 benchmark metrics drawn from
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| authoritative threat intelligence sources (Mandiant M-Trends, IBM CODB,
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| Verizon DBIR, CrowdStrike GTR, MITRE ATT&CK Evaluations, SANS, ENISA).
|
| The sample preserves the same calibration. Observed values on this sample:
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|
|
| | Test | Target | Observed | Verdict |
|
| |------|--------|----------|---------|
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| | dwell_time_hours_apt | 21.0000 | 21.1595 | ✓ PASS |
|
| | detection_rate_advanced | 0.8600 | 0.8600 | ✓ PASS |
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| | exfiltration_success_rate | 0.3100 | 0.3000 | ✓ PASS |
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| | lateral_hop_rate_apt | 0.0720 | 0.0552 | ✓ PASS |
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| | suppressed_alert_rate | 0.0770 | 0.0719 | ✓ PASS |
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| | mttd_hours_advanced | 4.2000 | 3.3541 | ✓ PASS |
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| | mttr_hours_advanced | 18.0000 | 19.7415 | ✓ PASS |
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| | ransomware_deployment_rate | 0.2400 | 0.2100 | ✓ PASS |
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| | campaign_success_rate | 0.3400 | 0.4300 | ~ MARGINAL |
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| | privilege_escalation_rate | 0.6200 | 0.6600 | ✓ PASS |
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| | edr_block_rate | 0.4300 | 0.3680 | ~ MARGINAL |
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| | supply_chain_compromise_rate | 0.0850 | 0.0800 | ✓ PASS |
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|
|
| *Note: some benchmarks (e.g. APT dwell time, MTTR) require larger sample
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| sizes to converge. The full product passes all 12 benchmarks at Grade A-.*
|
|
|
| ## Schema Highlights
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|
|
| ### `attack_events.csv` (primary file, timestep-level)
|
|
|
| | Column | Type | Description |
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| |------------------------------|---------|----------------------------------------------|
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| | campaign_id | string | Parent campaign FK |
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| | attacker_id | string | Attacker FK |
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| | timestep | int | Step in kill-chain simulation |
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| | phase | string | 1 of 9 ATT&CK phases |
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| | technique_id | string | MITRE ATT&CK technique ID (e.g. T1059.001) |
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| | technique_name | string | Human-readable technique name |
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| | tactic | string | ATT&CK tactic category |
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| | segment_id | string | FK to `network_topology.csv` |
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| | asset_id | string | Target asset within segment |
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| | attacker_tier | string | opportunistic / organized_crime / apt / nation_state |
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| | defender_maturity | string | ad_hoc / defined / managed / quant / optimizing |
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| | stealth_score | float | Action stealth weight (0–1) |
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| | detected | int | Whether action was detected (0/1) |
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| | blocked | int | Whether action was blocked (0/1) |
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| | edr_deployed | int | EDR present on target asset |
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| | alert_severity | string | INFO / LOW / MEDIUM / HIGH / CRITICAL |
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| | dwell_hours_so_far | float | Cumulative dwell time at this step |
|
|
|
| ### `campaign_summary.csv` (per-campaign outcome)
|
|
|
| | Column | Type | Description |
|
| |---------------------------------|---------|--------------------------------------------|
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| | campaign_id, attacker_id | string | Identifiers |
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| | attacker_tier, defender_maturity| string | Categorical |
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| | campaign_outcome | string | success / detected / blocked / aborted |
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| | total_dwell_hours | float | End-to-end attacker dwell time |
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| | mttd_hours, mttr_hours | float | Mean time to detect / respond |
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| | exfiltrated_bytes | int | Bytes exfiltrated (0 if none) |
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| | ransomware_deployed | int | Boolean |
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| | lateral_hops | int | Count of lateral movement actions |
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| | privilege_escalated | int | Boolean |
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| | supply_chain_used | int | Boolean |
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|
|
| See `campaign_events.csv` and `network_topology.csv` for the discrete event
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| 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)
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| - **Campaign outcome prediction** (success / detected / blocked / aborted)
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| - **MTTD / MTTR forecasting** under varying defender maturity
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| - **Ransomware risk modelling** across attacker tiers
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| - **Red-team simulation training data** for purple-team exercises
|
| - **SOC alert triage** benchmarking with realistic severity distributions
|
|
|
| ## Loading the Data
|
|
|
| ```python
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| import pandas as pd
|
|
|
| attacks = pd.read_csv("attack_events.csv")
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| campaigns = pd.read_csv("campaign_summary.csv")
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| events = pd.read_csv("campaign_events.csv")
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| topology = pd.read_csv("network_topology.csv")
|
|
|
| # Join to get the full attack context
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| enriched = attacks.merge(campaigns, on=["campaign_id", "attacker_id"], how="left")
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| enriched = enriched.merge(topology, on="segment_id", how="left")
|
|
|
| # Binary detection target
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| y = attacks["detected"].astype(int)
|
|
|
| # Campaign-level outcome target
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| y_outcome = campaigns["campaign_outcome"]
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| ```
|
|
|
| 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)},
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| author = {XpertSystems.ai},
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| year = {2026},
|
| url = {https://huggingface.co/datasets/xpertsystems/cyb002-sample}
|
| }
|
| ```
|
|
|
| ## Generation Details
|
|
|
| - Generator version : 2.0.0
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| - Random seed : 42
|
| - Generated : 2026-05-16 13:39:22 UTC
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| - Kill-chain model : 9-phase MITRE ATT&CK state machine
|
| - Overall benchmark : 95.3 / 100 (grade A)
|
|
|
