Efficient Hybrid Machine Learning and Feature Selection Approach for IoMT Attack Detection and Healthcare Security Enhancement

Abstract

The increasing interconnectivity of healthcare devices through the Internet of Medical Things (IoMT) has improved patient monitoring and treatment, but also exposed these systems to malicious cyberattacks that threaten both patient safety and data integrity. Existing machine learning (ML)-based approaches have attempted to detect such attacks, but most rely on all dataset features, including irrelevant and redundant ones, which increases computational cost and reduces detection accuracy. Feature selection techniques such as Particle Swarm Optimization (PSO) have been used to address this challenge, yet their default fitness functions fail to select the most suitable features for each classifier, often leading to suboptimal results. To overcome these limitations, this study introduces a novel fitness function integrated with PSO and ML classifiers to identify the most relevant features for accurate attack detection in IoMT devices. The proposed framework was evaluated using the NSL-KDD dataset (41 features) with RF, KNN, SVM, and LR classifiers. The number of correctly predicted labels for the optimal feature subsets was 99.35% for RF, 99.02% for KNN, 98.20% for SVM, and 97.61% for LR, whereas the baseline accuracies for the cases with all the features were 95.41%, 94.76%, 92.86%, and 89.55%, respectively. Moreover, the execution times decreased by almost one-third, showing the efficiency of the method. The report indicates validation of the PSO-based fitness function developed for lightweight-accuracy attacks detection in IoMT devices, thus proving to be efficient and cost-conducive, readily deployable in medical organizations as well as smart home environments, thereby safeguarding future-proof healthcare infrastructures against dynamically evolving threats.