| ---
|
| license: cc-by-nc-4.0
|
| task_categories:
|
| - tabular-classification
|
| - time-series-forecasting
|
| tags:
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| - cybersecurity
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| - ransomware
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| - threat-intelligence
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| - apt
|
| - synthetic-data
|
| - double-extortion
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| - backup-recovery
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| - mitre-attack
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| - incident-response
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| - raas
|
| pretty_name: CYB005 — Synthetic Ransomware Attack Simulation (Sample)
|
| size_categories:
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| - 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)
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| > — XGBoost + PyTorch MLP for **4-tier threat-actor attribution** (the
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| > README's stated headline use case), group-aware split by campaign,
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| > multi-seed evaluation (ROC-AUC 0.853 ± 0.031), honest leakage audit
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| > of every per-timestep feature.
|
|
|
| *Note: This sample is intentionally larger than the other CYB SKU samples.
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| CYB005 benchmarks are conditional on small actor-tier subsets (e.g.
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| 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 |
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| | `campaign_events.csv` | ~190,137 | ~60,000 | Discrete campaign event log |
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| | `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,
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| raas_affiliate, nation_state_nexus — with per-tier encryption speed,
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| ransom demand distributions, wiper component probabilities, and lateral
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| movement aggression
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| - **6 victim backup maturity tiers**: no_backup, local_only, network_attached,
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| cloud_replicated, immutable_object_lock, air_gapped_gold_standard — with
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| empirically-calibrated recovery probabilities
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| - **8 segment types**: corporate_lan, dmz, cloud_workload, ot_ics_control,
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| endpoint_subnet, soc_management, zero_trust_zone, backup_repository
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| - **7 attack phases**: initial_access, persistence, privilege_escalation,
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| lateral_movement, data_exfiltration, encryption_deployment, ransom_demand
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| - **Double extortion modeling** (data exfiltration + encryption)
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| - **VSS (Volume Shadow Copy) deletion**, wiper components, and worm spread
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| - **Living-off-the-Land (LotL)** abuse and EDR signature lag modeling
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| - **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
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| authoritative ransomware threat intelligence sources (Mandiant M-Trends,
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| 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 |
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| | actor_capability_tier | string | lone_actor / organised_syndicate / raas_affiliate / nation_state_nexus |
|
| | segment_id | string | FK to `victim_topology.csv` |
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| | backup_maturity_tier | string | 6 tiers from no_backup to air_gapped |
|
| | endpoints_compromised | int | Cumulative endpoints affected |
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| | 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"))
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| enriched = enriched.merge(topology, on="segment_id", how="left")
|
|
|
| # Actor-tier classification target
|
| y_tier = summaries["actor_capability_tier"]
|
|
|
| # Binary outcomes
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| 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+)
|
|
|
