Heterogeneous IoT/IIoT dataset from UNSW Canberra Cyber Range with network traffic, Windows/Linux OS traces, and IoT sensor telemetry. Labeled for 9 attack types including DoS, DDoS, ransomware, and XSS. CSV and PCAP formats. Benchmark for AI-based IDS evaluation.
Real IoT malware traffic dataset with 325M labeled network flows from 20 malware and 3 benign device captures over 500+ hours. PCAP and Zeek conn.log formats. Used for IoT botnet detection, malware traffic classification, and ML security research.
Real-time IoT network security dataset from a live IoT infrastructure with 41 bidirectional flow features. Includes ThingSpeak-LED, Wipro-Bulb, and MQTT-Temp devices with SSH brute force, DDoS (Hping/Slowloris), and Nmap attack scenarios. CSV format. Used for adaptive IDS development.
Realistic IoT/IIoT cybersecurity dataset supporting centralized and federated learning with 15 attack types across network, application, and protocol layers. CSV and PCAP formats (~12 GB). Available via IEEE Dataport and Kaggle. Designed for edge computing IDS research.
ToN_IoT is a large-scale dataset featuring heterogeneous data from IoT sensors, operating systems, and network traffic for advanced intrusion detection research in Industry 4.0.
A comprehensive and realistic IoT dataset generated by the Canadian Institute for Cybersecurity (CIC) for profiling, detecting, and characterizing multi-vector IoT attacks in a real network topology.