Developing a Federated, Lightweight, Interpretable, and Specialized AI-Based Intrusion Detection System for Medical Internet of Things (IoMT) Environments

Abstract

The rapid expansion of the Internet of Medical Things (IoMT) has enhanced healthcare services, but it has also exposed these systems to a growing range of cyberattacks. Addressing this challenge requires effective intrusion detection solutions. Machine learning-based intrusion detection systems (IDSs) offer a promising approach. In this research, we leverage IoMT-TrafficData, a recently published dataset specific to IoMT environments, to develop intrusion detection models based on federated learning (FL), which preserves data privacy while enabling the use of lightweight learning algorithms suited to resource-constrained devices. In addition, SHAP-based interpretability methods are employed to explain model decisions, thereby improving reliability and transparency. Compared with existing IDS solutions for IoMT, this research proposes an approach that balances performance, privacy, and interpretability while accounting for resource limitations, resulting in three unified, lightweight, and interpretable detection models tailored to different data types in IoMT environments. This work thus provides a solid foundation for future research on secure medical IoT systems.