---
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**