Network-traffic dataset on Mendeley Data documenting DDoS attacks against the Fibaro Home Center 3 smart-home controller; PCAP and CSV formats are provided. [page:4][web:52]
Sensor recordings of activities by a single user in a smart home, using PIR, reed switches, force sensors, light, temperature/humidity and smart plugs; includes three CSV files for analysis.
IoT-23 provides labeled IoT network-traffic captures, including 20 malware scenarios and 3 benign IoT captures, intended to support machine-learning research on IoT security.
A longitudinal visual dataset of urban streetlight scenes captured daily over several years including 2025, accompanied by structured metadata for smart city monitoring, drift detection, and anomaly analysis. Includes over 526,000 images with timestamps and GPS metadata, enabling vision-based model training in urban environments.
Longitudinal ambient sensor data collected from 4 community homes featuring motion sensors, door sensors, and temperature readings with activity labels. This dataset captures naturalistic behavior patterns for activity recognition model development in real-world smart home environments. Data includes continuous recordings from PIR motion sensors, magnetic door sensors, and ambient temperature sensors with annotated activities by external annotators. The dataset provides a valuable resource for building activity recognition models that operate in uncontrolled, naturalistic settings.
Published in November 2025, this dataset provides a multimodal framework integrating satellite imagery and IoT sensor data for environmental monitoring and disaster management. It is designed to support the development of machine learning models that synchronize remote sensing with ground-based IoT observations for real-time risk assessment.
AIR4LIFE is a high-resolution air quality monitoring dataset published in late 2025, featuring measurements from a dual-node IoT setup. It tracks pollutants and ambient conditions to evaluate the trade-offs between energy-aware duty cycling and data completeness in environmental sensing networks.
CICIoT2023 is a large-scale, flow-based network traffic dataset capturing real-time benign and malicious communications in an IoT environment composed of 105 physical devices. The dataset captures traffic traces for 33 attack scenarios grouped into seven categories: DDoS, DoS, Reconnaissance, web-based attacks, brute-force attempts, spoofing, and Mirai malware.
The Industrial IoT Dataset (Synthetic) provides a large-scale simulation of sensor readings and operational metrics from machines deployed in a smart factory environment. It focuses on predictive maintenance and anomaly detection.
UrbanAirNet provides a comprehensive collection of urban air quality and weather parameters measured via IoT sensor networks. It includes pollutants like PM2.5, NO2, and O3 alongside meteorological variables.
IMAD-DS captures multi-rate, multi-sensor signals from scaled industrial machines, including a robotic arm and a brushless motor, for anomaly detection research.
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.