Large-scale IoT cybersecurity dataset with 47M+ labeled network flows from 105 real IoT devices across 33 attack types in 7 categories. PCAP and CSV formats. Built for IDS/IPS development and ML-based IoT traffic classification research.
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.
Free CC0 synthetic dataset: 10,000 rows of Modbus, OPC UA and DNP3 flows labelled for SCADA intrusion detection. 16% Attacks. Reproducible from its seed.
Free CC0 synthetic dataset: 10,000 rows of Modbus, OPC UA and DNP3 flows labelled for SCADA intrusion detection. 15% Attacks. Reproducible from its seed.
Reproducible large-scale IoT network dataset from 78 emulated devices using MQTT, CoAP, and RTSP protocols. Includes benign and malicious traffic with DoS, brute force, scanning, and C&C attacks in PCAP and CSV formats.
Latest 2026 IoT malware dataset from the Canadian Institute for Cybersecurity (CIC) and Yunnan University, featuring comprehensive malware samples and behavioral analysis data for IoT threat detection research.