README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - cybersecurity
  - ransomware
  - threat-intelligence
  - apt
  - synthetic-data
  - double-extortion
  - backup-recovery
  - mitre-attack
  - incident-response
  - raas
pretty_name: CYB005 — Synthetic Ransomware Attack Simulation (Sample)
size_categories:
  - 10K<n<100K
---

# CYB005 — Synthetic Ransomware Attack Simulation Dataset (Sample)

**XpertSystems.ai Synthetic Data Platform · SKU: CYB005-SAMPLE · Version 1.0.0**

This is a **free preview** of the full **CYB005 — Synthetic Ransomware Attack
Simulation Dataset** product. It contains roughly **~10% of the full
dataset** at identical schema, actor-tier distribution, and statistical
fingerprint, so you can evaluate fit before licensing the full product.

> 🤖 **Trained baseline available:**
> [**xpertsystems/cyb005-baseline-classifier**](https://huggingface.co/xpertsystems/cyb005-baseline-classifier)
> — XGBoost + PyTorch MLP for **4-tier threat-actor attribution** (the
> README's stated headline use case), group-aware split by campaign,
> multi-seed evaluation (ROC-AUC 0.853 ± 0.031), honest leakage audit
> of every per-timestep feature.

*Note: This sample is intentionally larger than the other CYB SKU samples.
CYB005 benchmarks are conditional on small actor-tier subsets (e.g.
nation_state campaigns are ~10% of the fleet), so a larger sample is needed
to demonstrate the full product's benchmark calibration reliably.*

| File                         | Rows (sample) | Rows (full)   | Description                                  |
|------------------------------|---------------|---------------|----------------------------------------------|
| `victim_topology.csv`        | ~300           | ~3,200        | Network segment registry                     |
| `campaign_summary.csv`       | ~500            | ~5,500        | Per-campaign outcome aggregates              |
| `campaign_events.csv`        | ~190,137         | ~60,000       | Discrete campaign event log                  |
| `attack_timelines.csv`       | ~37,489         | ~290,000      | Per-timestep campaign trajectory data        |

## Dataset Summary

CYB005 simulates end-to-end ransomware campaign lifecycles as a **7-phase
state machine** across enterprise, cloud, and OT/ICS environments, with:

- **4 actor capability tiers**: lone_actor, organised_syndicate,
  raas_affiliate, nation_state_nexus — with per-tier encryption speed,
  ransom demand distributions, wiper component probabilities, and lateral
  movement aggression
- **6 victim backup maturity tiers**: no_backup, local_only, network_attached,
  cloud_replicated, immutable_object_lock, air_gapped_gold_standard — with
  empirically-calibrated recovery probabilities
- **8 segment types**: corporate_lan, dmz, cloud_workload, ot_ics_control,
  endpoint_subnet, soc_management, zero_trust_zone, backup_repository
- **7 attack phases**: initial_access, persistence, privilege_escalation,
  lateral_movement, data_exfiltration, encryption_deployment, ransom_demand
- **Double extortion modeling** (data exfiltration + encryption)
- **VSS (Volume Shadow Copy) deletion**, wiper components, and worm spread
- **Living-off-the-Land (LotL)** abuse and EDR signature lag modeling
- **Financial impact scoring** with ransom demand × payment probability

## Trained Baseline Available

A working baseline classifier trained on this sample is published at
**[xpertsystems/cyb005-baseline-classifier](https://huggingface.co/xpertsystems/cyb005-baseline-classifier)**.

| Component | Detail |
|---|---|
| Task | **4-class threat-actor capability-tier attribution** (the README's headline use case) |
| Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
| Features | 63 (after one-hot encoding); pipeline included as `feature_engineering.py` |
| Split | **Group-aware by campaign_id** — train/val/test campaigns disjoint |
| Validation | Single seed + multi-seed aggregate across 10 seeds |
| Demo | `inference_example.ipynb` — end-to-end copy-paste |
| Headline metrics | XGBoost: accuracy 0.603 ± 0.040, macro ROC-AUC 0.853 ± 0.031 (multi-seed) |

This is the **first XpertSystems baseline to ship the dataset's stated
headline use case** (rather than pivoting to a phase-prediction subtask
as the smaller CYB002 / CYB003 / CYB004 samples required). CYB005's
500-campaign sample is large enough that tier attribution learns
honestly under group-aware splitting.

## Calibrated Benchmark Targets

The full product is calibrated to 12 benchmark metrics drawn from
authoritative ransomware threat intelligence sources (Mandiant M-Trends,
CrowdStrike GTR, Coveware Quarterly Ransomware Report, Sophos State of
Ransomware, IBM CODB, Verizon DBIR, CISA #StopRansomware, Chainalysis).
The sample preserves the same calibration:

| Test | Target | Observed | Verdict |
|------|--------|----------|---------|
| 01_blast_radius_pct_organised_syndicate_low_seg | 0.3700 | 0.3302 | ✓ PASS |
| 02_dwell_time_pre_detonation_hrs_median | 204.0000 | 226.1000 | ✓ PASS |
| 03_ransom_paid_rate_all_tiers | 0.2900 | 0.2941 | ✓ PASS |
| 04_recovery_without_payment_rate_immutable | 0.7200 | 0.7292 | ✓ PASS |
| 05_double_extortion_rate_raas_syndicate | 0.7700 | 0.7400 | ✓ PASS |
| 06_mttd_hrs_global_median | 192.0000 | 203.5600 | ✓ PASS |
| 07_ransom_demand_usd_median_raas | 650,000 | 633,445 | ✓ PASS |
| 08_vss_deletion_success_rate | 0.6800 | 0.6529 | ✓ PASS |
| 09_edr_alert_rate_per_lateral_move | 0.5400 | 0.5123 | ✓ PASS |
| 10_wiper_component_rate_nation_state | 0.2200 | 0.2933 | ~ MARGINAL |
| 11_backup_destruction_rate_weak_tiers | 0.4200 | 0.4126 | ✓ PASS |
| 12_financial_impact_score_syndicate | 0.6100 | 0.5810 | ✓ PASS |

*Note: some benchmarks (e.g. wiper component rate, blast radius) require
larger sample sizes to converge tightly because they're conditional on
small-population subsets (e.g. nation-state campaigns are ~10% of fleet).
The full product passes all 12 benchmarks at Grade A+ or better.*

## Schema Highlights

### `attack_timelines.csv` (primary file, per-timestep)

| Column                          | Type    | Description                                  |
|---------------------------------|---------|----------------------------------------------|
| campaign_id                     | string  | Unique campaign identifier                   |
| actor_id                        | string  | Threat actor ID                              |
| timestep                        | int     | Step in 7-phase lifecycle (0–74)             |
| campaign_phase                  | string  | 1 of 7 phases                                |
| actor_capability_tier           | string  | lone_actor / organised_syndicate / raas_affiliate / nation_state_nexus |
| segment_id                      | string  | FK to `victim_topology.csv`                  |
| backup_maturity_tier            | string  | 6 tiers from no_backup to air_gapped         |
| endpoints_compromised           | int     | Cumulative endpoints affected                |
| blast_radius_pct                | float   | Fleet-wide compromise percentage             |
| lateral_pivots                  | int     | Lateral movement count                       |
| edr_alerted                     | int     | Boolean — EDR alert raised                   |
| siem_correlated                 | int     | Boolean — SIEM correlation event             |
| lotl_technique_used             | string  | LotL binary if any                           |
| vss_deletion_attempted          | int     | Boolean — Volume Shadow Copy deletion        |
| wiper_component_deployed        | int     | Boolean — destructive wiper present          |
| data_exfiltrated_gb             | float   | Cumulative exfiltrated data                  |
| dwell_hours                     | float   | Cumulative attacker dwell time               |
| c2_beacon_active                | int     | C2 channel beaconing flag                    |

### `campaign_summary.csv` (per-campaign outcome)

| Column                          | Type    | Description                                  |
|---------------------------------|---------|----------------------------------------------|
| campaign_id, actor_id           | string  | Identifiers                                  |
| actor_capability_tier           | string  | Tier classification target                   |
| backup_maturity_tier            | string  | Victim backup posture                        |
| campaign_outcome                | string  | success / partial / detected / aborted       |
| ransom_demand_usd               | float   | Ransom amount demanded                       |
| ransom_paid_flag                | int     | Boolean — ransom paid                        |
| recovery_without_payment_flag   | int     | Boolean — restored from backup               |
| double_extortion_flag           | int     | Boolean — data leak threat                   |
| wiper_component_flag            | int     | Boolean — wiper deployed                     |
| dwell_time_pre_detonation_hrs   | float   | Hours from access to encryption              |
| mean_time_to_detect_hrs         | float   | Hours from access to first detection         |
| financial_impact_score          | float   | Composite impact score (0–1)                 |
| blast_radius_pct                | float   | Fleet compromise percentage                  |

See `campaign_events.csv` and `victim_topology.csv` for the discrete event
log and segment registry schemas respectively.

## Suggested Use Cases

- Training **ransomware classifier** models —
  [worked example available](https://huggingface.co/xpertsystems/cyb005-baseline-classifier)
- **Backup posture risk modeling** — predict recovery likelihood from
  6-tier backup maturity
- **Dwell time forecasting** under varying actor capability and defender
  maturity
- **Double extortion prediction** (data theft + encryption modeling)
- **Wiper component detection** — distinguishing destructive vs financial
  ransomware
- **VSS deletion / shadow copy abuse** detection
- **Financial impact estimation** — ransom demand + payment probability
- **EDR alert correlation** — SIEM signal-to-noise modeling
- **Incident response simulation** — purple-team exercises with calibrated
  attacker behavior

## Loading the Data

```python
import pandas as pd

timelines  = pd.read_csv("attack_timelines.csv")
summaries  = pd.read_csv("campaign_summary.csv")
events     = pd.read_csv("campaign_events.csv")
topology   = pd.read_csv("victim_topology.csv")

# Join per-timestep data with campaign-level labels and topology
enriched = timelines.merge(summaries, on=["campaign_id", "actor_id"], how="left",
                           suffixes=("", "_summary"))
enriched = enriched.merge(topology, on="segment_id", how="left")

# Actor-tier classification target
y_tier = summaries["actor_capability_tier"]

# Binary outcomes
y_paid   = summaries["ransom_paid_flag"]
y_recovered = summaries["recovery_without_payment_flag"]
y_wiper  = summaries["wiper_component_flag"]
```

For a worked end-to-end example with actor-tier classification,
group-aware splitting, and feature engineering, see the inference notebook
in the [baseline classifier repo](https://huggingface.co/xpertsystems/cyb005-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 CYB005 dataset includes **~358,000 rows** across all four files,
with calibrated benchmark validation against 12 metrics drawn from
authoritative ransomware threat intelligence sources.

📧  **pradeep@xpertsystems.ai**
🌐  **https://xpertsystems.ai**

## Citation

```bibtex
@dataset{xpertsystems_cyb005_sample_2026,
  title  = {CYB005: Synthetic Ransomware Attack Simulation Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb005-sample}
}
```

## Generation Details

- Generator version : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-16 14:03:22 UTC
- Campaign model    : 7-phase ransomware kill-chain state machine
- Overall benchmark : 97.7 / 100  (grade A+)