| --- |
| license: cc-by-4.0 |
| language: |
| - fr |
| - en |
| tags: |
| - cybersecurity |
| - anomaly-detection |
| - network-logs |
| - intrusion-detection |
| - synthetic |
| - nids |
| - siem |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - text-classification |
| - token-classification |
| pretty_name: Network Anomaly Logs — French Industrial Infrastructure |
| --- |
| |
| # 🔐 Network Anomaly Logs — French Industrial Infrastructure |
|
|
| ## Description |
|
|
| **50,000 synthetic network logs** from French industrial infrastructure environments, with expert anomaly annotations. |
|
|
| Built by a cybersecurity practitioner with real-world experience in VLAN segmentation, pfSense, and Zabbix monitoring environments. Data is realistic, structured, and ready to use for ML training. |
|
|
| --- |
|
|
| ## 📊 Statistics |
|
|
| | Split | Examples | Normal | Anomalies | |
| |-------|----------|--------|-----------| |
| | Train | 40,000 | 34,000 | 6,000 | |
| | Val | 5,000 | 4,250 | 750 | |
| | Test | 5,000 | 4,250 | 750 | |
| | **Total** | **50,000** | **42,500** | **7,500** | |
|
|
| - **Anomaly rate:** 15% |
| - **Format:** JSONL (one example per line) |
|
|
| --- |
|
|
| ## 🚨 Anomaly Types |
|
|
| | Type | Description | Severity | |
| |------|-------------|----------| |
| | `port_scan` | Sequential port scanning | Medium | |
| | `data_exfiltration` | Large outbound transfer to unknown IP | Critical | |
| | `brute_force_ssh` | Repeated failed SSH login attempts | High | |
| | `ddos_flood` | Volumetric UDP/ICMP flood | Critical | |
| | `c2_communication` | Periodic C2 server beaconing | Critical | |
| | `lateral_movement` | Internal machine-to-machine movement | High | |
| | `dns_tunneling` | Data exfiltration via DNS queries | High | |
| | `credential_dump` | Access to authentication resources | Critical | |
|
|
| --- |
|
|
| ## 📋 Schema |
|
|
| Each example contains the following fields: |
|
|
| ```json |
| { |
| "id": "log_00000001", |
| "timestamp": "2025-01-01T00:29:52.311Z", |
| "src_ip": "10.10.30.229", |
| "src_port": 64132, |
| "src_vlan": "CLI", |
| "dst_ip": "194.165.84.245", |
| "dst_port": 443, |
| "dst_vlan": "EXTERNAL", |
| "protocol": "TCP", |
| "bytes_sent": 1240, |
| "bytes_received": 8430, |
| "packets_sent": 3, |
| "packets_received": 12, |
| "duration_ms": 234, |
| "ttl": 128, |
| "tcp_flags": "ACK", |
| "user_agent": null, |
| "is_anomaly": false, |
| "anomaly_type": null, |
| "severity": null, |
| "description": null, |
| "label": "normal_https_browse" |
| } |
| ``` |
|
|
| --- |
|
|
| ## 🏗️ Infrastructure Context |
|
|
| Logs simulate a segmented French industrial network with 4 VLANs: |
|
|
| | VLAN | Subnet | Role | |
| |------|--------|------| |
| | ADM | 10.10.10.0/24 | Administration | |
| | SRV | 10.10.20.0/24 | Servers | |
| | CLI | 10.10.30.0/24 | Clients | |
| | EXTERNAL | — | Internet | |
|
|
| --- |
|
|
| ## 🎯 Use Cases |
|
|
| - Network Intrusion Detection Systems (NIDS) |
| - Anomaly detection model training & fine-tuning |
| - Cybersecurity benchmark evaluation |
| - SIEM rule validation & testing |
| - ML research on network security |
|
|
| --- |
|
|
| ## 📦 Access |
|
|
| This dataset is **gated** — request access using the button above. |
|
|
| Access is free. Once approved, you will receive a download link with: |
| - ✅ Full 50,000 examples (train / val / test splits) |
| - ✅ Python generation script |
| - ✅ Commercial use license (CC BY 4.0) |
|
|
| --- |
|
|
| ## 📜 License |
|
|
| [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) |
|
|
| Commercial use allowed with attribution. |
|
|
| --- |
|
|
| ## 📬 Contact & Custom Datasets |
|
|
| Need a custom dataset with specific anomaly types, volume, or format? |
|
|
| 📧 Contact: **soon mail incoming** |
