README.md

---
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**](https://huggingface.co/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](https://huggingface.co/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](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
- **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

```python
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](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
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,
  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+)