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cyb003-sample

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Tabular Classification
Time Series Forecasting
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cybersecurity
malware
malware-classification
threat-intelligence
apt
ransomware
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license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - cybersecurity
  - malware
  - malware-classification
  - threat-intelligence
  - apt
  - ransomware
  - synthetic-data
  - edr
  - sandbox-evasion
  - polymorphic
pretty_name: CYB003 — Synthetic Malware Behaviour & Classification (Sample)
size_categories:
  - 1K<n<10K

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+)