RT-IoT2022 is a network traffic dataset specifically derived from a real-time IoT testbed containing smart home devices. It includes both normal traffic and various common network attacks.
The Gotham Dataset is a large-scale, reproducible benchmark for evaluating decentralized Intrusion Detection Systems (IDS) and Federated Learning in virtualized smart cities. It captures interface-level network traffic from 78 heterogeneous IoT devices, including complex attack vectors like Mirai botnets, Merlin C2 traffic, and CoAP amplification, preserving the non-IID nature of edge data for realistic AI security training.
This Industrial IoT dataset provides synthetic yet realistic sensor data simulating equipment operation under normal and various failure conditions. Designed for predictive maintenance and machine learning, it includes sensor specifications, operational thresholds, and failure labels, allowing researchers to develop anomaly detection models without the constraints of sensitive real-world industrial data.
CeTI-Age-Kinematics is a full-body IMU kinematics dataset of 30 daily tasks recorded with a 19-IMU sensor suit in an age-comparative sample of 32 participants (older adults and younger controls), intended for motion analysis and activity recognition research.
GAITEX is a comprehensive multimodal human motion dataset capturing impaired gait and rehabilitation exercises using nine wearable IMUs and optical motion capture systems, designed for biomechanical analysis and rehabilitation monitoring.
MQTT_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.
DataSense is a real-time Industrial IoT (IIoT) dataset from the Canadian Institute for Cybersecurity, combining synchronized sensor and network data from a 40-device testbed with over 15 types of industrial sensors for anomaly and intrusion detection research.
A synthetic but carefully constructed IoT dataset for smart home renewable energy management, providing five CSV datasets (20, 50, 100, 200 homes over 365 days) with daily energy consumption and production values to simulate small, medium, and large-scale smart city and smart home scenarios.
An image dataset of 3,200 manually annotated RGB images covering 32 classes of IoT education kits (Arduino, ESP32, Raspberry Pi, etc.), collected from multiple sources and annotated with polygon masks via Roboflow for object detection research in educational IoT kits.
A real-world household dataset from 13 residential properties in Portugal over nearly three years, with 15-minute resolution measurements of electrical load, solar PV generation, weather parameters, and electricity market prices, published in Nature Scientific Data.
IoT-DH is a real-world IoT DDoS honeypot dataset collected from a honeypot deployment and converted from PCAP to CSV with traffic features and labels for DDoS classification, identification, and detection tasks.
A pair of labeled MQTT and UDP DDoS datasets for Healthcare-IoT networks, generated with Cooja and ns-3 simulators to support evaluation of DDoS detection and mitigation techniques in H-IoT environments.