--- 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 🤖 **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+)