UL-ECE-DDoS-H-IoT-Datasets2025

Published: 18 June 2025| Version 2 | DOI: 10.17632/2bw34ght8b.2
Contributors:
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Description

This dataset includes two labeled CSV files generated to evaluate Distributed Denial-of-Service (DDoS) detection and mitigation techniques in healthcare-IoT (H-IoT) environments. The datasets are generated from simulations of network traffic involving H-IoT devices, such as body temperature, oxygen saturation, and heart rate sensors, using the MQTT and UDP protocols via the Cooja and ns-3 simulators. The research article "TCN-Based DDoS Detection and Mitigation in 5G Healthcare-IoT: A Frequency Monitoring and Dynamic Threshold Approach" utilizes these datasets, published in IEEE Access (DOI: 10.1109/ACCESS.2025.3531659). Each file contains tabular features representing H-IoT node behavior and an outcome column indicating whether the sample is normal (0) or an attack (1). Preprocessing scripts and raw data are available at: https://github.com/mirzaakhi/UL-ECE-DDoS-H-IoT-Datasets2025

Files

Steps to reproduce

Preprocessing steps and raw data files used to generate the final datasets are available in the linked GitHub repository: https://github.com/mirzaakhi/UL-ECE-DDoS-H-IoT-Datasets2025

Institutions

University of Limerick

Categories

Computer Science, Cybersecurity, Machine Learning, Denial-of-Service Attack, Internet of Things, Healthcare Research, Deep Learning

Funding

Taighde Éireann - Research Ireland

18/CRT/6049

Licence