xpertsystems/cyb001-sample

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.

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.

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

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)

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+)