| ---
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| license: cc-by-nc-4.0
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| task_categories:
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| - tabular-classification
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| - time-series-forecasting
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| tags:
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| - cybersecurity
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| - malware
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| - malware-classification
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| - threat-intelligence
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| - apt
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| - ransomware
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| - synthetic-data
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| - edr
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| - sandbox-evasion
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| - polymorphic
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| pretty_name: CYB003 — Synthetic Malware Behaviour & Classification (Sample)
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| size_categories:
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| - 1K<n<10K
|
| ---
|
|
|
| # CYB003 — Synthetic Malware Behaviour & Classification Dataset (Sample)
|
|
|
| **XpertSystems.ai Synthetic Data Platform · SKU: CYB003-SAMPLE · Version 1.0.0**
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|
|
| This is a **free preview** of the full **CYB003 — Synthetic Malware Behaviour
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| & 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.
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|
|
| > 🤖 **Trained baseline available:**
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| > [**xpertsystems/cyb003-baseline-classifier**](https://huggingface.co/xpertsystems/cyb003-baseline-classifier)
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| > — XGBoost + PyTorch MLP for 10-class malware execution-phase prediction,
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| > group-aware split by sample, multi-seed evaluation (accuracy 0.905 ± 0.010),
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| > honest disclosure of which tasks need the full dataset.
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|
|
| | File | Rows (sample) | Rows (full) | Description |
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| |----------------------------|---------------|---------------|------------------------------------------------|
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| | `environment_profiles.csv` | ~100 | ~3,200 | Endpoint environment configurations |
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| | `sample_summary.csv` | ~100 | ~5,600 | Per-sample aggregate KPIs |
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| | `execution_events.csv` | ~1,056 | ~60,000 | Discrete malware lifecycle events |
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| | `malware_samples.csv` | ~6,000 | ~280,000 | Per-timestep sample telemetry |
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|
|
| ## Dataset Summary
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|
|
| CYB003 simulates malware execution lifecycles across endpoint protection
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| stacks with calibrated detection/evasion outcomes, covering:
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|
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| - **9 malware families**: ransomware, trojan, rootkit, worm, spyware,
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| fileless_malware, cryptominer, botnet_agent, dropper
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| - **4 threat-actor tiers**: commodity, crimeware, apt, nation_state — with
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| per-tier sandbox evasion budgets, LotL (Living-off-the-Land) abuse rates,
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| and polymorphic mutation probabilities
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| - **Endpoint protection stacks**: legacy AV, NGAV (ML-based), EDR
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| - **Static PE features**: entropy, packing detection, section anomalies,
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| import hash distributions
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| - **Behavioural telemetry**: process injection, persistence mechanisms,
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| C2 beacon patterns, lateral spread
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| - **Outcome modelling**: AV signature detection, EDR behavioural detection,
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| sandbox evasion success, family attribution confidence
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|
|
| ## Trained Baseline Available
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|
|
| A working baseline classifier trained on this sample is published at
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| **[xpertsystems/cyb003-baseline-classifier](https://huggingface.co/xpertsystems/cyb003-baseline-classifier)**.
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|
|
| | Component | Detail |
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| |---|---|
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| | Task | 10-class malware execution-phase classification |
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| | Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
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| | Features | 69 (after one-hot encoding); pipeline included as `feature_engineering.py` |
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| | Split | **Group-aware by sample_id** — train/val/test samples disjoint |
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| | Validation | Single seed + multi-seed aggregate across 10 seeds |
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| | Demo | `inference_example.ipynb` — end-to-end copy-paste |
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| | Headline metrics | XGBoost: accuracy 0.905 ± 0.010, macro ROC-AUC 0.975 ± 0.002 (multi-seed) |
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|
|
| The model card documents an honest finding worth knowing before licensing:
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| **malware-family classification is at majority baseline on the sample's 100
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| samples** (a sample-size constraint, not a method failure — the full
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| 280k-row dataset has ~5,600 samples and supports family classification
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| properly). The baseline pivots to **execution-phase prediction**, which is
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| strongly learnable on the sample data (91% accuracy, ROC-AUC 0.98, stable
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| across 10 seeds) and is itself a real SOC use case for dynamic-analysis
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| and EDR phase tagging.
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|
|
| ## Calibrated Benchmark Targets
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|
|
| The full product is calibrated to 12 benchmark metrics drawn from
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| authoritative threat intelligence and AV-testing sources (VirusTotal,
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| AV-TEST, MITRE ATT&CK Evaluations, Mandiant M-Trends, CrowdStrike GTR,
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| Verizon DBIR). The sample preserves the same calibration:
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|
|
| | Test | Target | Observed | Verdict |
|
| |------|--------|----------|---------|
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| | av_detection_rate_commodity | 0.6200 | 0.6319 | ✓ PASS |
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| | edr_detection_rate_apt | 0.3100 | 0.3096 | ✓ PASS |
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| | sandbox_evasion_rate_nation | 0.7200 | 0.7225 | ✓ PASS |
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| | lateral_propagation_rate | 0.0950 | 0.1038 | ✓ PASS |
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| | pe_entropy_mean_packed | 0.9100 | 0.9100 | ✓ PASS |
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| | lotl_abuse_rate_apt | 0.4300 | 0.4300 | ✓ PASS |
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| | dwell_time_ratio_apt | 0.3200 | 0.3198 | ✓ PASS |
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| | family_attribution_confidence | 0.6800 | 0.6808 | ✓ PASS |
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| | c2_detection_rate | 0.5400 | 0.5394 | ✓ PASS |
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| | campaign_success_rate | 0.3400 | 0.2900 | ✓ PASS |
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| | polymorphic_detection_penalty | 0.2400 | 0.2392 | ✓ PASS |
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| | false_negative_rate_fileless | 0.3800 | 0.4203 | ✓ PASS |
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|
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| *Note: some benchmarks (e.g. campaign success rate, lateral propagation)
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| require larger sample sizes to converge tightly. The full product passes
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| all 12 benchmarks at Grade A- or better.*
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|
|
| ## Schema Highlights
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|
|
| ### `malware_samples.csv` (primary file, per-timestep telemetry)
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|
|
| | Column | Type | Description |
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| |------------------------------|---------|----------------------------------------------|
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| | sample_id | string | Unique malware sample identifier |
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| | family_id | string | Malware family instance ID |
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| | actor_id | string | Threat actor ID |
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| | timestep | int | Step in malware lifecycle (0–59) |
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| | malware_family | string | 1 of 9 families |
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| | threat_actor_tier | string | commodity / crimeware / apt / nation_state |
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| | target_platform | string | windows / linux / macos / android |
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| | ep_stack | string | legacy_av / ngav_ml_based / edr_full |
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| | pe_entropy | float | Portable Executable section entropy (0–1) |
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| | packer_detected_flag | int | Whether PE packer was detected |
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| | process_injection_count | int | Process-injection events at this step |
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| | persistence_mechanism | string | registry / scheduled_task / service / wmi |
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| | c2_beacon_active | int | Whether C2 channel is beaconing |
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| | sandbox_evaded | int | Whether sandbox evasion succeeded |
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| | av_detected | int | AV signature detection at this step |
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| | edr_detected | int | EDR behavioural detection at this step |
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| | dwell_time_hours | float | Cumulative dwell time |
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| | lotl_technique_used | string | Living-off-the-Land binary if any |
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|
|
| ### `sample_summary.csv` (per-sample outcome)
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|
|
| | Column | Type | Description |
|
| |-----------------------------------|---------|------------------------------------------|
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| | sample_id, family_id, actor_id | string | Identifiers |
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| | malware_family | string | Family classification target |
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| | threat_actor_tier | string | Tier classification target |
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| | target_platform | string | Platform |
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| | campaign_success_flag | int | Boolean — successful campaign |
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| | av_detection_flag | int | Boolean — AV detection ever |
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| | edr_detection_flag | int | Boolean — EDR detection ever |
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| | sandbox_evaded_flag | int | Boolean — sandbox evasion ever |
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| | packer_detected_flag | int | Boolean — packer detected |
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| | family_attribution_confidence | float | Confidence score (0–1) |
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| | total_dwell_hours | float | End-to-end dwell |
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| | lateral_propagation_count | int | Count of lateral spread events |
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|
|
| See `execution_events.csv` and `environment_profiles.csv` for the discrete
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| event log and endpoint environment schemas respectively.
|
|
|
| ## Suggested Use Cases
|
|
|
| - Training **malware execution-phase classifiers** —
|
| [worked example available](https://huggingface.co/xpertsystems/cyb003-baseline-classifier)
|
| - 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
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| - **Sandbox evasion detection** with tier-calibrated evasion budgets
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| - **Polymorphic malware detection** — sample mutation effects on AV
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| signature coverage
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| - **C2 beacon detection** with realistic beacon-active timestep patterns
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| - **PE entropy / packing detection** — entropy distributions tied to
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| ground-truth packing flags
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| - **Living-off-the-Land binary detection** for APT-tier samples
|
|
|
| ## Loading the Data
|
|
|
| ```python
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| import pandas as pd
|
|
|
| samples = pd.read_csv("malware_samples.csv")
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| summaries = pd.read_csv("sample_summary.csv")
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| events = pd.read_csv("execution_events.csv")
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| environments = pd.read_csv("environment_profiles.csv")
|
|
|
| # Join per-timestep telemetry with per-sample summary labels
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| enriched = samples.merge(summaries, on="sample_id", how="left",
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| suffixes=("", "_summary"))
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|
|
| # Family classification target
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| y_family = summaries["malware_family"]
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|
|
| # Threat-actor tier target
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| y_tier = summaries["threat_actor_tier"]
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|
|
| # Binary detection target (per-timestep)
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| y_detected = (samples["av_detected"] | samples["edr_detected"]).astype(int)
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| ```
|
|
|
| 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](https://huggingface.co/xpertsystems/cyb003-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
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| 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
|
|
|
| ```bibtex
|
| @dataset{xpertsystems_cyb003_sample_2026,
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| title = {CYB003: Synthetic Malware Behaviour & Classification Dataset (Sample)},
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| author = {XpertSystems.ai},
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| 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+)
|
|
|
