README.md

---
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - time-series-forecasting
tags:
  - cybersecurity
  - network-traffic
  - intrusion-detection
  - synthetic-data
  - anomaly-detection
  - apt
  - c2-beacon
pretty_name: CYB001 — Synthetic Network Traffic (Sample)
size_categories:
  - 1K<n<10K
---

# CYB001 — Synthetic Network Traffic Dataset (Sample)

**XpertSystems.ai Synthetic Data Platform · SKU: CYB001-SAMPLE · Version 1.0.0**

This is a **free preview** of the full **CYB001 — Synthetic Network Traffic
Dataset** product. It contains roughly **1 / 60th of the full dataset** at
identical schema, label distribution, and statistical fingerprint, so you can
evaluate fit before licensing the full product.

> 🤖 **Trained baseline available:**
> [**xpertsystems/cyb001-baseline-classifier**](https://huggingface.co/xpertsystems/cyb001-baseline-classifier)
> — XGBoost + PyTorch MLP, copy-paste inference notebook, full metrics and
> honest limitations in the model card.

| File                    | Rows (sample) | Rows (full)   | Description                                |
|-------------------------|---------------|---------------|--------------------------------------------|
| `network_topology.csv`  | ~200   | ~3,200        | Network segments and defender configs      |
| `session_summary.csv`   | ~1000    | ~62,000       | Multi-flow session aggregates              |
| `network_flows.csv`     | ~9,770    | ~500,000      | Per-flow records (CICFlowMeter-compatible) |
| `flow_events.csv`       | ~5,431    | ~120,000      | Per-flow security event records            |

## Dataset Summary

CYB001 simulates 30 days of enterprise network traffic across **9 segment
types** (corporate LAN, DMZ, cloud workload, OT/ICS, endpoint fleet, SOC
management plane, zero-trust, guest Wi-Fi, data centre spine), with:

- **3-class labels**: `BENIGN`, `MALICIOUS`, `AMBIGUOUS`
- **19 fine-grained traffic categories** including portscan, brute-force,
  SQLi, XSS, exfiltration, C2 beaconing, lateral movement, ransomware staging
- **4 attacker capability tiers**: opportunistic, targeted, APT, insider threat
- **Diurnal traffic patterns** with off-peak attack bias for APT/insider tiers
- **APT C2 beacon regularity** governed by a configurable IAT coefficient of
  variation (default 0.065 → regularity score ≈ 0.93)

All IP addresses are SHA-256 pseudonyms (`IP_<12 hex>`) — no real network data.

## Trained Baseline Available

A working baseline classifier trained on this sample is published at
**[xpertsystems/cyb001-baseline-classifier](https://huggingface.co/xpertsystems/cyb001-baseline-classifier)**.

| Component | Detail |
|---|---|
| Task | 3-class flow classification (`BENIGN` / `MALICIOUS` / `AMBIGUOUS`) |
| Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
| Features | 101 (after one-hot encoding); pipeline included as `feature_engineering.py` |
| Demo | `inference_example.ipynb` — end-to-end copy-paste |
| Headline metrics | XGBoost test accuracy 0.998, macro-F1 0.996 — synthetic; see model card for limitations |

This is a reference baseline, not a production IDS. The model card documents
the calibrated signals it picks up, an ablation showing the model is not
session-dominated, and six explicit limitations including the gap between
synthetic and real-world traffic.

## Calibrated Benchmark Targets

The full product is calibrated to 12 benchmark metrics; the sample preserves
the same calibration. Observed values on this sample:

| Test | Target | Observed | Verdict |
|------|--------|----------|---------|
| malicious_flow_rate | 0.1720 | 0.2015 | ✓ PASS |
| c2_beacon_regularity_score | 0.7800 | 0.7673 | ✓ PASS |
| payload_entropy_benign_mean | 4.8000 | 4.8556 | ✓ PASS |
| protocol_violation_rate | 0.0150 | 0.0161 | ✓ PASS |
| scan_probe_density | 0.0430 | 0.0450 | ✓ PASS |
| exfil_volume_ratio | 0.0240 | 0.0153 | ✓ PASS |
| retransmission_rate | 0.0380 | 0.0365 | ✓ PASS |
| dns_query_rate_anomaly | 0.0620 | 0.0620 | ✓ PASS |
| lateral_move_flag_rate | 0.0310 | 0.0340 | ✓ PASS |
| session_risk_score_apt | 0.6800 | 0.6803 | ✓ PASS |
| fwd_bwd_byte_ratio_benign | 1.3400 | 1.4119 | ✓ PASS |
| tunnel_detection_rate | 0.0180 | 0.0180 | ✓ PASS |

## Schema

### `network_flows.csv` (primary file)

| Column                       | Type    | Description                                  |
|------------------------------|---------|----------------------------------------------|
| flow_id                      | string  | Unique flow identifier                       |
| session_id                   | string  | Parent session FK                            |
| source_ip_hash               | string  | SHA-256 pseudonymised source IP              |
| destination_ip_hash          | string  | SHA-256 pseudonymised destination IP         |
| source_port, dest_port       | int     | TCP/UDP port numbers                         |
| protocol                     | string  | TCP / UDP / HTTPS / DNS / SMTP / SSH / etc.  |
| flow_start_timestamp         | string  | ISO timestamp                                |
| flow_duration_ms             | int     | Flow duration in milliseconds                |
| total_fwd_packets            | int     | Forward packet count                         |
| total_bwd_packets            | int     | Backward packet count                        |
| total_bytes_fwd              | int     | Forward byte volume                          |
| total_bytes_bwd              | int     | Backward byte volume                         |
| fwd_packet_len_mean / _std   | int     | Forward packet length statistics             |
| bwd_packet_len_mean / _std   | int     | Backward packet length statistics            |
| flow_bytes_per_sec           | float   | Throughput (bytes/sec)                       |
| flow_packets_per_sec         | float   | Throughput (packets/sec)                     |
| inter_arrival_time_mean/_std | float   | IAT statistics (ms) — key C2 beacon feature  |
| tcp_flag_{syn,ack,fin,rst,psh,urg}_count | int | TCP flag counts                  |
| flow_lifecycle_phase         | string  | initiation / handshake / transfer / etc.     |
| traffic_category             | string  | 1 of 19 fine-grained categories              |
| attack_subcategory           | string  | Attack subcategory (empty for benign)        |
| **label**                    | string  | **BENIGN / MALICIOUS / AMBIGUOUS** (target)  |
| segment_id                   | string  | FK to `network_topology.csv`                 |
| source_device_type           | string  | workstation / server / iot / mobile / cloud  |
| dest_device_type             | string  | (same as source)                             |
| attacker_capability_tier     | string  | opportunistic / targeted / apt / insider     |
| retransmission_flag          | int     | TCP retransmission flag                      |
| fragmentation_flag           | int     | IP fragmentation flag                        |
| protocol_violation_flag      | int     | Protocol-violation detection flag            |

See `session_summary.csv`, `network_topology.csv`, and `flow_events.csv` for
the complementary aggregate, topology, and per-event schemas.

## Suggested Use Cases

- Training and evaluating **network intrusion detection** models
- Benchmarking **C2 beacon detection** algorithms (regular-IAT signatures)
- **APT behaviour modelling** with off-peak temporal bias
- **Multi-class anomaly detection** including the `AMBIGUOUS` class
- **Synthetic-vs-real transfer-learning** studies
- **Feature engineering** practice with CICFlowMeter-compatible fields

## Loading the Data

```python
import pandas as pd

flows    = pd.read_csv("network_flows.csv")
sessions = pd.read_csv("session_summary.csv")
topology = pd.read_csv("network_topology.csv")
events   = pd.read_csv("flow_events.csv")

# Join flows to topology to get defender configuration
flows_enriched = flows.merge(topology, on="segment_id", how="left")

# Binary classification target
y = (flows["label"] == "MALICIOUS").astype(int)
```

For a worked end-to-end example including the 3-class classification target,
feature engineering, and predictions, see the inference notebook in the
[baseline classifier repo](https://huggingface.co/xpertsystems/cyb001-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 CYB001 dataset includes **~685,000 rows** across all four files,
with calibrated A-grade benchmark validation across 12 statistical tests.

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

## Citation

```bibtex
@dataset{xpertsystems_cyb001_sample_2026,
  title  = {CYB001: Synthetic Network Traffic Dataset (Sample)},
  author = {XpertSystems.ai},
  year   = {2026},
  url    = {https://huggingface.co/datasets/xpertsystems/cyb001-sample}
}
```

## Generation Details

- Generator version : 1.0.0
- Random seed       : 42
- Generated         : 2026-05-16 13:23:38 UTC
- Simulation window : 30 days
- Overall benchmark : 100.0 / 100  (grade A+)