EDR-FJ48: An Empirical Distribution Ranking-Based Fuzzy J48 Classifier for Multiclass Intrusion Detection in IoMT Networks

The Internet of Medical Things (IoMT) interconnects medical devices, software applications, and healthcare services through the internet to enable the transmission and analysis of health data. IoMT facilitates seamless patient care and supports real-time clinical decision-making. The IoMT faces substantial security threats due to limited device resources, high device interconnectivity, and a lack of standardization. In this paper, we present an Intrusion Detection System (IDS) called An Empirical Distribution Ranking-Based Fuzzy J48 Classifier for Multiclass Intrusion Detection in IoMT Networks (EDR-FJ48) to distinguish between regular traffic and multiple types of security threats. The proposed IDS is built upon the J48 decision tree algorithm and is designed to detect a wide range of attacks. To ensure the protection of medical devices and patient data, the system incorporates a fuzzy IF-THEN rule inference module. In our approach, fuzzy rules are formulated based on the fuzzified values of selected features, which capture the statistical behavior of the input observations. These rules enable interpretable and transparent decision-making and are applied before the final classification step. We thoroughly evaluated our methodology through extensive simulations using three publicly available datasets, such as WUSTL-EHMS-2020, CICIoMT2024, and ECU-IoHT. The results exhibit exceptional accuracy rates of 99.68%, 98.71%, and 99.43%, respectively. A comparative analysis against state-of-the-art models in the existing literature, based on metrics including accuracy, precision, recall, F1-score, and time complexity, reveals that our proposed method achieves superior results. This evidence suggests that our method constitutes a robust solution for mitigating security threats in IoMT networks.