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.
Multi-source healthcare dataset integrating Electronic Health Records, medical imaging (CT and MRI scans), and wearable IoT sensor data for personalized treatment optimization. Includes 5,008 brain imaging files and real-time physiological monitoring data.
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.
Real-world IoT sensor dataset for precision agriculture and plant health monitoring. Includes environmental parameters (temperature, humidity, light) and soil metrics (pH, moisture, temperature) with Arduino-ESP8266 integration and cloud transmission.
Enhanced smart home energy consumption dataset with minute-resolution monitoring of 13+ appliances and regional weather data. Includes traditional appliances plus new IoT devices like car chargers, water heaters, pool pumps, and outdoor lighting.
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.
Dataset with 500 controlled simulation scenarios analyzing ICSHSO-based dynamic optimization for energy efficiency in wireless sensor networks. Includes network lifetime, PDR, residual energy, and transmission reduction metrics.
Curated IoT network traffic dataset for intelligent network management and resource allocation research. Features diverse device types, traffic patterns, and quality-of-service metrics for ML-based optimization.
Comprehensive network traffic dataset from UNSW Canberra Cyber Range Lab capturing benign and malicious flows in simulated IoT/IIoT smart environments using Argus and Zeek (Bro) tools.
Realistic cybersecurity dataset with 14 attack types from 10+ IoT/IIoT device types including sensors, actuators, and industrial controllers. Supports centralized and federated learning with 61 optimized features.