A federated learning evaluation across several contemporary IoT and IIoT intrusion detection datasets, benchmarking algorithms such as FedAvg, FedProx, and FedNova with LSTM and Transformer models in in-domain, cross-dataset, and multi-dataset federation scenarios.
Deep 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.
Multimodal dataset combining Text-to-SQL natural language queries with IoT network traffic classification, featuring 10,985 SQL training examples and labeled network traffic (benign/malicious) from IoT-23 and Smart Building sensors for NLP and security research.
Comprehensive Industrial IoT security dataset from the Canadian Institute for Cybersecurity, featuring realistic network traffic with 34 types of attacks including DDoS, ransomware, data exfiltration, and advanced persistent threats across multiple IIoT protocols.
Large-scale reproducible IoT network dataset with traffic from 100+ diverse IoT devices including smart home, wearable, and industrial sensors, featuring multiple attack scenarios and benign behavior for intrusion detection research.
Real in-vehicle CAN bus traffic from a Kia Soul logged via OBD-II port, including normal operation and three types of message injection attacks (DoS, fuzzy, impersonation) for intrusion detection research.[web:224][web:225][web:221]
Real-time network traffic dataset from diverse IoT devices including normal behavior and various attacks (DDoS, brute-force, scans) for developing intrusion detection systems.
Dataset for evaluating federated learning approaches to IoT intrusion detection published in Nature Scientific Reports January 2026. Features distributed network traffic from multiple IoT deployments with privacy constraints and decentralized learning evaluation metrics.
State-of-the-art IIoT dataset from Canadian Institute for Cybersecurity with synchronized sensor and network data from 40 devices including 15+ industrial sensors. Features multi-objective feature selection for anomaly detection in industrial environments.
Comprehensive 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.
Comprehensive large-scale IoT intrusion detection dataset from Canadian Institute for Cybersecurity with 33 attack types across 105 real IoT devices. Includes 8.94 GB of network traffic data covering DDoS, DoS, Mirai, MITM, and reconnaissance attacks.
New 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.