xpertsystems/cyb005-sample

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 — 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.

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
• 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

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.

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

@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+)