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 — 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.
| 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
AMBIGUOUSclass - Synthetic-vs-real transfer-learning studies
- Feature engineering practice with CICFlowMeter-compatible fields
Loading the Data
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.
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
@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+)