MQTT IoT IDS dataset from a simulated network with 12 sensors, broker, camera, and attacker. PCAP and CSV features support MQTT intrusion detection research.
IoT botnet IDS dataset using 83 real and emulated devices with Mirai, BashLite, and Torii traffic. Raw PCAP files support botnet and anomaly detection research.
Comprehensive energy dataset from 255 UK homes covering electricity, gas, room temperature, humidity, and appliance-level data for a 39-home sub-cohort. 23 months of data. CSV format. Published on Edinburgh DataShare (DOI: 10.7488/ds/2836). Described in Nature Scientific Data 2021.
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.
Benchmark bearing vibration dataset from Case Western Reserve University with drive-end and fan-end faults at 4 severity levels. Sampled at 12 kHz and 48 kHz. MATLAB MAT and CSV formats. Used for fault diagnosis and vibration-based condition monitoring.
Long-duration smart-home utility dataset with two years of minutely electricity, water, and natural gas measurements plus weather and billing data. CSV/TSV/RData formats. Used for forecasting, NILM, and resource analytics.
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.
BCCC-IoT-IDS-Zwave-2025 is a behavior-centric cybersecurity dataset focusing on Z-wave protocol vulnerabilities and intrusion detection for modern smart home automation systems.
MC-MED provides high-resolution multimodal clinical and physiological data from 118,385 adult emergency department visits, supporting real-time monitoring and AI-based medical research.
MIMIC-III is a globally recognized database featuring de-identified health data from 40,000+ ICU patients, integrating vital signs, lab results, and IoT device outputs for research.
MuST-C is a multi-sensor agricultural dataset for in-field phenotyping, covering six crop species with RGB, LiDAR, and multispectral data to automate large-scale growth monitoring.