Smart Home Intrusion Detection Dataset โ 7 Attack Scenarios
Smart home traffic captured under normal operation and seven multi-stage attack scenarios across heterogeneous end devices. CC BY 4.0, published 2026.
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Smart home traffic captured under normal operation and seven multi-stage attack scenarios across heterogeneous end devices. CC BY 4.0, published 2026.
View DatasetIndustrial IoT IDS dataset with labelled TCP/IP and DNP3 flow statistics plus PCAP files for 9 SCADA cyberattacks. CSV and PCAP formats for ML/DL IDS research.
View DatasetFree CC0 synthetic dataset: 4,000 rows of Modbus, OPC UA and DNP3 flows labelled for SCADA intrusion detection. 21% Attacks. Reproducible from its seed.
View DatasetFree CC0 synthetic dataset: 10,000 rows of Modbus, OPC UA and DNP3 flows labelled for SCADA intrusion detection. 29% Attacks. Reproducible from its seed.
View DatasetComprehensive synthetic dataset designed for analyzing DDoS attacks in Internet of Things environments.
View DatasetA 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.
View DatasetDataset documents replay attacks targeting MQTT communications in water distribution system with 4.8 MB CSV file containing original and replayed messages for temporal sequence analysis.
View DatasetCICIoT2023 is a large-scale, flow-based network traffic dataset capturing real-time benign and malicious communications in an IoT environment composed of 105 physical devices. The dataset captures traffic traces for 33 attack scenarios grouped into seven categories: DDoS, DoS, Reconnaissance, web-based attacks, brute-force attempts, spoofing, and Mirai malware.
View DatasetMQTT_UAD is a public MQTT traffic dataset published in Data in Brief 2025, containing labeled benign and attack scenarios in IoT networks that use the MQTT protocol, designed for training and evaluating intrusion detection systems.
View DatasetDeep learning-ready dataset combining real vehicle CAN bus traffic and simulated attack scenarios (DoS, fuzzing, spoofing) for training intrusion detection systems to protect autonomous and connected vehicles from cyber-attacks.
View DatasetComprehensive large-scale IoT botnet dataset combining legitimate IoT network traffic with realistic botnet attack scenarios. Features full packet captures (PCAP) and extracted flow features for diverse attack types including DDoS, reconnaissance, theft, and DoS attacks.
View DatasetNew realistic IoT network intrusion dataset (MU-IoT) with comprehensive attack scenarios for cybersecurity research. Published in IEEE 2024 with 4+ citations. Covers multiple IoT protocols and device types.
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