Network-Based Multi-Protocol Attack Detection in Internet-of-Medical-Things Using Explainable Machine Learning

Internet of Things applications are on the rise within different areas of health and medical service. With this rapid rise of these applications, threat actors become more interested in targeting such devices. Within the health and medical context, there are particular challenges in privacy and security. In this paper, we present an explainable machine learning model designed to detect multi-protocol network-based attacks on Internet-of-Medical-Things with high accuracy. The proposed system was trained and tested using CICIoMT-2024 dataset. The proposed system delivered an accuracy exceeding $99.9 \%$, with an $F_{1}$ score exceeding 0.99. To increase trust in the obtained results, the proposed system was explained using SHAP values to provide insights into the most impactful features, and the nature of their impact on the system’s decisions.