Efficient Intrusion Detection for IoMT: Integrating Machine Learning, Feature Selection, and Fuzzy Logic

Abstract

The internet of medical things (IoMT) has transformed healthcare by enabling real-time patient monitoring, remote diagnoses, and effective data exchange among connected medical devices and clinical systems. The increasing reliance on interconnected medical equipment has also intensified cybersecurity risks, as resource-constrained devices and wireless communication channels are vulnerable to attacks such as man-in-the-middle, spoofing, data injection, and ransomware. Intrusion Detection Systems (IDSs) play a critical role in mitigating these threats; however, traditional IDS approaches often struggle with high-dimensional IoMT data, class imbalance, and uncertainty in traffic patterns, which can increase false alarms and reduce reliability in safety-critical environments. This dissertation investigates efficient and deployable IDS designs for IoMT networks by integrating machine learning, feature selection, and fuzzy logic to improve detection reliability while reducing model complexity.

First, the dissertation provide an extensive examination of IDS approaches proposed for IoMT, classifying them into machine learning, deep learning, fuzzy logic , and hybrid categories, while analyzing IoMT architectures and security vulnerabilities across layers.

Next, it develops an efficient IDS model based on machine learning classifiers combined with feature selection techniques to enhanced detection accuracy and reduce computational cost in edge and gateway settings. Building on this direction, the dissertation proposed a multi-level feature selection pipeline that combines complementary ranking methods and consensus selection to identify consistently informative features, followed by a fuzzy inference system that supports uncertainty-aware intrusion classification using interpretable rule-based reasoning.

The suggested IDS systems exhibit robust detection capabilities with reduced false-alarm rates, utilizing small feature sets appropriate for gateway and edge deployment throughout benchmark tests. The dissertation presents a cohesive security system that prioritizes efficiency, interpretability, and practical implementation for the protection of IoMT communications and the safeguarding of sensitive healthcare information. Future works will expand these IDS designs to include other IoMT datasets and real network traffic, while further investigating robustness in the context of concept drift and increasing adversarial strategies, all while maintaining low complexity and transparency.