| ---
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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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| - network-traffic
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| - intrusion-detection
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| - synthetic-data
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| - anomaly-detection
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| - apt
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| - c2-beacon
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| pretty_name: CYB001 — Synthetic Network Traffic (Sample)
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| size_categories:
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| - 1K<n<10K
|
| ---
|
|
|
| # CYB001 — Synthetic Network Traffic Dataset (Sample)
|
|
|
| **XpertSystems.ai Synthetic Data Platform · SKU: CYB001-SAMPLE · Version 1.0.0**
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|
|
| This is a **free preview** of the full **CYB001 — Synthetic Network Traffic
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| Dataset** product. It contains roughly **1 / 60th of the full dataset** at
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| identical schema, label distribution, and statistical fingerprint, so you can
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| evaluate fit before licensing the full product.
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|
|
| > 🤖 **Trained baseline available:**
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| > [**xpertsystems/cyb001-baseline-classifier**](https://huggingface.co/xpertsystems/cyb001-baseline-classifier)
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| > — XGBoost + PyTorch MLP, copy-paste inference notebook, full metrics and
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| > honest limitations in the model card.
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|
|
| | File | Rows (sample) | Rows (full) | Description |
|
| |-------------------------|---------------|---------------|--------------------------------------------|
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| | `network_topology.csv` | ~200 | ~3,200 | Network segments and defender configs |
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| | `session_summary.csv` | ~1000 | ~62,000 | Multi-flow session aggregates |
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| | `network_flows.csv` | ~9,770 | ~500,000 | Per-flow records (CICFlowMeter-compatible) |
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| | `flow_events.csv` | ~5,431 | ~120,000 | Per-flow security event records |
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|
|
| ## Dataset Summary
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|
|
| CYB001 simulates 30 days of enterprise network traffic across **9 segment
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| types** (corporate LAN, DMZ, cloud workload, OT/ICS, endpoint fleet, SOC
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| management plane, zero-trust, guest Wi-Fi, data centre spine), with:
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|
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| - **3-class labels**: `BENIGN`, `MALICIOUS`, `AMBIGUOUS`
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| - **19 fine-grained traffic categories** including portscan, brute-force,
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| SQLi, XSS, exfiltration, C2 beaconing, lateral movement, ransomware staging
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| - **4 attacker capability tiers**: opportunistic, targeted, APT, insider threat
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| - **Diurnal traffic patterns** with off-peak attack bias for APT/insider tiers
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| - **APT C2 beacon regularity** governed by a configurable IAT coefficient of
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| variation (default 0.065 → regularity score ≈ 0.93)
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|
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| All IP addresses are SHA-256 pseudonyms (`IP_<12 hex>`) — no real network data.
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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/cyb001-baseline-classifier](https://huggingface.co/xpertsystems/cyb001-baseline-classifier)**.
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|
|
| | Component | Detail |
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| |---|---|
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| | Task | 3-class flow classification (`BENIGN` / `MALICIOUS` / `AMBIGUOUS`) |
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| | Models | XGBoost (`model_xgb.json`) + PyTorch MLP (`model_mlp.safetensors`) |
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| | Features | 101 (after one-hot encoding); pipeline included as `feature_engineering.py` |
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| | Demo | `inference_example.ipynb` — end-to-end copy-paste |
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| | Headline metrics | XGBoost test accuracy 0.998, macro-F1 0.996 — synthetic; see model card for limitations |
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|
|
| This is a reference baseline, not a production IDS. The model card documents
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| the calibrated signals it picks up, an ablation showing the model is not
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| session-dominated, and six explicit limitations including the gap between
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| synthetic and real-world traffic.
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|
|
| ## Calibrated Benchmark Targets
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|
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| The full product is calibrated to 12 benchmark metrics; the sample preserves
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| the same calibration. Observed values on this sample:
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|
|
| | Test | Target | Observed | Verdict |
|
| |------|--------|----------|---------|
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| | malicious_flow_rate | 0.1720 | 0.2015 | ✓ PASS |
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| | c2_beacon_regularity_score | 0.7800 | 0.7673 | ✓ PASS |
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| | payload_entropy_benign_mean | 4.8000 | 4.8556 | ✓ PASS |
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| | protocol_violation_rate | 0.0150 | 0.0161 | ✓ PASS |
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| | scan_probe_density | 0.0430 | 0.0450 | ✓ PASS |
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| | exfil_volume_ratio | 0.0240 | 0.0153 | ✓ PASS |
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| | retransmission_rate | 0.0380 | 0.0365 | ✓ PASS |
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| | dns_query_rate_anomaly | 0.0620 | 0.0620 | ✓ PASS |
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| | lateral_move_flag_rate | 0.0310 | 0.0340 | ✓ PASS |
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| | session_risk_score_apt | 0.6800 | 0.6803 | ✓ PASS |
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| | fwd_bwd_byte_ratio_benign | 1.3400 | 1.4119 | ✓ PASS |
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| | tunnel_detection_rate | 0.0180 | 0.0180 | ✓ PASS |
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|
|
| ## Schema
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|
|
| ### `network_flows.csv` (primary file)
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|
|
| | Column | Type | Description |
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| |------------------------------|---------|----------------------------------------------|
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| | flow_id | string | Unique flow identifier |
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| | session_id | string | Parent session FK |
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| | source_ip_hash | string | SHA-256 pseudonymised source IP |
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| | destination_ip_hash | string | SHA-256 pseudonymised destination IP |
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| | source_port, dest_port | int | TCP/UDP port numbers |
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| | protocol | string | TCP / UDP / HTTPS / DNS / SMTP / SSH / etc. |
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| | flow_start_timestamp | string | ISO timestamp |
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| | flow_duration_ms | int | Flow duration in milliseconds |
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| | total_fwd_packets | int | Forward packet count |
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| | total_bwd_packets | int | Backward packet count |
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| | total_bytes_fwd | int | Forward byte volume |
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| | total_bytes_bwd | int | Backward byte volume |
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| | fwd_packet_len_mean / _std | int | Forward packet length statistics |
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| | bwd_packet_len_mean / _std | int | Backward packet length statistics |
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| | flow_bytes_per_sec | float | Throughput (bytes/sec) |
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| | flow_packets_per_sec | float | Throughput (packets/sec) |
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| | inter_arrival_time_mean/_std | float | IAT statistics (ms) — key C2 beacon feature |
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| | tcp_flag_{syn,ack,fin,rst,psh,urg}_count | int | TCP flag counts |
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| | flow_lifecycle_phase | string | initiation / handshake / transfer / etc. |
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| | traffic_category | string | 1 of 19 fine-grained categories |
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| | attack_subcategory | string | Attack subcategory (empty for benign) |
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| | **label** | string | **BENIGN / MALICIOUS / AMBIGUOUS** (target) |
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| | segment_id | string | FK to `network_topology.csv` |
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| | source_device_type | string | workstation / server / iot / mobile / cloud |
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| | dest_device_type | string | (same as source) |
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| | attacker_capability_tier | string | opportunistic / targeted / apt / insider |
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| | retransmission_flag | int | TCP retransmission flag |
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| | fragmentation_flag | int | IP fragmentation flag |
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| | protocol_violation_flag | int | Protocol-violation detection flag |
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|
|
| See `session_summary.csv`, `network_topology.csv`, and `flow_events.csv` for
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| the complementary aggregate, topology, and per-event schemas.
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|
|
| ## Suggested Use Cases
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|
|
| - Training and evaluating **network intrusion detection** models
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| - Benchmarking **C2 beacon detection** algorithms (regular-IAT signatures)
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| - **APT behaviour modelling** with off-peak temporal bias
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| - **Multi-class anomaly detection** including the `AMBIGUOUS` class
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| - **Synthetic-vs-real transfer-learning** studies
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| - **Feature engineering** practice with CICFlowMeter-compatible fields
|
|
|
| ## Loading the Data
|
|
|
| ```python
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| import pandas as pd
|
|
|
| flows = pd.read_csv("network_flows.csv")
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| sessions = pd.read_csv("session_summary.csv")
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| topology = pd.read_csv("network_topology.csv")
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| events = pd.read_csv("flow_events.csv")
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|
|
| # Join flows to topology to get defender configuration
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| flows_enriched = flows.merge(topology, on="segment_id", how="left")
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|
|
| # Binary classification target
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| 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
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| 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,
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| title = {CYB001: Synthetic Network Traffic Dataset (Sample)},
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| author = {XpertSystems.ai},
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| year = {2026},
|
| url = {https://huggingface.co/datasets/xpertsystems/cyb001-sample}
|
| }
|
| ```
|
|
|
| ## Generation Details
|
|
|
| - Generator version : 1.0.0
|
| - Random seed : 42
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| - Generated : 2026-05-16 13:23:38 UTC
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| - Simulation window : 30 days
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| - Overall benchmark : 100.0 / 100 (grade A+)
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|
|
