xpertsystems/cyb003-sample

CYB003 — Synthetic Malware Behaviour & Classification Dataset (Sample)

XpertSystems.ai Synthetic Data Platform · SKU: CYB003-SAMPLE · Version 1.0.0

This is a free preview of the full CYB003 — Synthetic Malware Behaviour & Classification Dataset product. It contains roughly 1 / 56th of the full dataset at identical schema, family/tier distribution, and statistical fingerprint, so you can evaluate fit before licensing the full product.

🤖 Trained baseline available: xpertsystems/cyb003-baseline-classifier — XGBoost + PyTorch MLP for 10-class malware execution-phase prediction, group-aware split by sample, multi-seed evaluation (accuracy 0.905 ± 0.010), honest disclosure of which tasks need the full dataset.

File	Rows (sample)	Rows (full)	Description
environment_profiles.csv	~100	~3,200	Endpoint environment configurations
sample_summary.csv	~100	~5,600	Per-sample aggregate KPIs
execution_events.csv	~1,056	~60,000	Discrete malware lifecycle events
malware_samples.csv	~6,000	~280,000	Per-timestep sample telemetry

Dataset Summary

CYB003 simulates malware execution lifecycles across endpoint protection stacks with calibrated detection/evasion outcomes, covering:

• 9 malware families: ransomware, trojan, rootkit, worm, spyware, fileless_malware, cryptominer, botnet_agent, dropper
• 4 threat-actor tiers: commodity, crimeware, apt, nation_state — with per-tier sandbox evasion budgets, LotL (Living-off-the-Land) abuse rates, and polymorphic mutation probabilities
• Endpoint protection stacks: legacy AV, NGAV (ML-based), EDR
• Static PE features: entropy, packing detection, section anomalies, import hash distributions
• Behavioural telemetry: process injection, persistence mechanisms, C2 beacon patterns, lateral spread
• Outcome modelling: AV signature detection, EDR behavioural detection, sandbox evasion success, family attribution confidence

Trained Baseline Available

A working baseline classifier trained on this sample is published at xpertsystems/cyb003-baseline-classifier.

Component	Detail
Task	10-class malware execution-phase classification
Models	XGBoost (model_xgb.json) + PyTorch MLP (model_mlp.safetensors)
Features	69 (after one-hot encoding); pipeline included as feature_engineering.py
Split	Group-aware by sample_id — train/val/test samples disjoint
Validation	Single seed + multi-seed aggregate across 10 seeds
Demo	inference_example.ipynb — end-to-end copy-paste
Headline metrics	XGBoost: accuracy 0.905 ± 0.010, macro ROC-AUC 0.975 ± 0.002 (multi-seed)

The model card documents an honest finding worth knowing before licensing: malware-family classification is at majority baseline on the sample's 100 samples (a sample-size constraint, not a method failure — the full 280k-row dataset has ~5,600 samples and supports family classification properly). The baseline pivots to execution-phase prediction, which is strongly learnable on the sample data (91% accuracy, ROC-AUC 0.98, stable across 10 seeds) and is itself a real SOC use case for dynamic-analysis and EDR phase tagging.

Calibrated Benchmark Targets

The full product is calibrated to 12 benchmark metrics drawn from authoritative threat intelligence and AV-testing sources (VirusTotal, AV-TEST, MITRE ATT&CK Evaluations, Mandiant M-Trends, CrowdStrike GTR, Verizon DBIR). The sample preserves the same calibration:

Test	Target	Observed	Verdict
av_detection_rate_commodity	0.6200	0.6319	✓ PASS
edr_detection_rate_apt	0.3100	0.3096	✓ PASS
sandbox_evasion_rate_nation	0.7200	0.7225	✓ PASS
lateral_propagation_rate	0.0950	0.1038	✓ PASS
pe_entropy_mean_packed	0.9100	0.9100	✓ PASS
lotl_abuse_rate_apt	0.4300	0.4300	✓ PASS
dwell_time_ratio_apt	0.3200	0.3198	✓ PASS
family_attribution_confidence	0.6800	0.6808	✓ PASS
c2_detection_rate	0.5400	0.5394	✓ PASS
campaign_success_rate	0.3400	0.2900	✓ PASS
polymorphic_detection_penalty	0.2400	0.2392	✓ PASS
false_negative_rate_fileless	0.3800	0.4203	✓ PASS

Note: some benchmarks (e.g. campaign success rate, lateral propagation) require larger sample sizes to converge tightly. The full product passes all 12 benchmarks at Grade A- or better.

Schema Highlights

malware_samples.csv (primary file, per-timestep telemetry)

Column	Type	Description
sample_id	string	Unique malware sample identifier
family_id	string	Malware family instance ID
actor_id	string	Threat actor ID
timestep	int	Step in malware lifecycle (0–59)
malware_family	string	1 of 9 families
threat_actor_tier	string	commodity / crimeware / apt / nation_state
target_platform	string	windows / linux / macos / android
ep_stack	string	legacy_av / ngav_ml_based / edr_full
pe_entropy	float	Portable Executable section entropy (0–1)
packer_detected_flag	int	Whether PE packer was detected
process_injection_count	int	Process-injection events at this step
persistence_mechanism	string	registry / scheduled_task / service / wmi
c2_beacon_active	int	Whether C2 channel is beaconing
sandbox_evaded	int	Whether sandbox evasion succeeded
av_detected	int	AV signature detection at this step
edr_detected	int	EDR behavioural detection at this step
dwell_time_hours	float	Cumulative dwell time
lotl_technique_used	string	Living-off-the-Land binary if any

sample_summary.csv (per-sample outcome)

Column	Type	Description
sample_id, family_id, actor_id	string	Identifiers
malware_family	string	Family classification target
threat_actor_tier	string	Tier classification target
target_platform	string	Platform
campaign_success_flag	int	Boolean — successful campaign
av_detection_flag	int	Boolean — AV detection ever
edr_detection_flag	int	Boolean — EDR detection ever
sandbox_evaded_flag	int	Boolean — sandbox evasion ever
packer_detected_flag	int	Boolean — packer detected
family_attribution_confidence	float	Confidence score (0–1)
total_dwell_hours	float	End-to-end dwell
lateral_propagation_count	int	Count of lateral spread events

See execution_events.csv and environment_profiles.csv for the discrete event log and endpoint environment schemas respectively.

Suggested Use Cases

• Training malware execution-phase classifiers — worked example available
• Training malware family classifiers (9-class with realistic class imbalance and family-specific feature distributions — full dataset recommended for adequate per-class sample size)
• Threat actor attribution modelling (4-tier classification)
• EDR detection benchmarking — packed vs unpacked, signature vs behavioural, fileless vs binary
• Sandbox evasion detection with tier-calibrated evasion budgets
• Polymorphic malware detection — sample mutation effects on AV signature coverage
• C2 beacon detection with realistic beacon-active timestep patterns
• PE entropy / packing detection — entropy distributions tied to ground-truth packing flags
• Living-off-the-Land binary detection for APT-tier samples

Loading the Data

import pandas as pd

samples    = pd.read_csv("malware_samples.csv")
summaries  = pd.read_csv("sample_summary.csv")
events     = pd.read_csv("execution_events.csv")
environments = pd.read_csv("environment_profiles.csv")

# Join per-timestep telemetry with per-sample summary labels
enriched = samples.merge(summaries, on="sample_id", how="left",
                         suffixes=("", "_summary"))

# Family classification target
y_family = summaries["malware_family"]

# Threat-actor tier target
y_tier = summaries["threat_actor_tier"]

# Binary detection target (per-timestep)
y_detected = (samples["av_detected"] | samples["edr_detected"]).astype(int)

For a worked end-to-end example with execution-phase 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 CYB003 dataset includes ~349,000 rows across all four files, with calibrated benchmark validation against 12 metrics drawn from authoritative AV-testing and threat intelligence sources.

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

Citation

@dataset{xpertsystems_cyb003_sample_2026,
  title  = {CYB003: Synthetic Malware Behaviour & Classification Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb003-sample}
}

Generation Details

• Generator version : 1.0.0
• Random seed : 42
• Generated : 2026-05-16 13:46:05 UTC
• Lifecycle model : Multi-timestep PE + behavioural + outcome simulation
• Overall benchmark : 100.0 / 100 (grade A+)