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 collection of research papers on IoT attacks and security models published between 2005-2025 in IEEE, Elsevier, Springer, ACM, and MDPI, compiled for systematic review of IoT network security across all layers, enabling meta-analysis and trend identification.
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-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.
Specialized dataset for detecting IoT botnet attacks using network traffic analysis. Captures behavior of 9 real IoT devices infected with Mirai and BASHLITE malware variants. Ideal for training ML models to identify compromised IoT devices through traffic patterns.
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.
1,000 records of simulated IoT network activity with blockchain-based security. Covers DDoS, malware, MITM attacks across device, network, and application layers.
The most cited cybersecurity dataset worldwide with 2.8+ million network flows capturing 14 types of realistic attack scenarios including DDoS, brute force, botnet, and web attacks alongside benign traffic for advanced intrusion detection systems.
Real-time physiological and network-level data from a secure IoT healthcare monitoring system tracking 2000 patients, including biometric readings (heart rate, temperature, blood pressure) and network metadata for anomaly detection and cybersecurity analysis.