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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+)
|