AQU-IMF-RFE: an extended feature selection method for intrusion detection in IoMT data using aquila optimization-based mutual information and recursive feature elimination

The Internet of Medical Things (IoMT) is a revolutionary advancement in the healthcare field, allowing real-time monitoring and data gathering from medical devices attached to the human body. However, the sensitive nature of physiological data traveling over wireless networks makes IoMT networks extra sensitive to advanced cyber-attacks. Conventional IDS approaches often struggle with overfitting and fail to generalize well in dynamic attack scenarios, particularly in resource-constrained environments. To address this challenge, this study proposes a novel hybrid feature selection method, AQU-IMF-RFE, which integrates Aquila Optimization (AO) and Mutual Information (MI) with Recursive Feature Elimination (RFE). The methodology begins by calculating the mutual information scores for each feature to capture relevance, followed by optimization using the AO algorithm to handle redundancy and identify an optimal subset of features. Finally, RFE was applied to refine the selection through iterative feature ranking using machine learning feedback. This three-stage approach enhances the detection accuracy, reduces the dimensionality, and ensures computational feasibility. The method was tested on three benchmark IoMT datasets—UNSW-NB15, BOT-IOT, and CICIDS2017—with excellent classification accuracies of 94.35%, 94.48%, and 92.26%, respectively–demonstrating its effectiveness. These results not only outperform current state-of-the-art methods but also indicate the efficacy of the proposed method in identifying a wide variety of attacks. Thus, the proposed AQU-IMF-RFE framework represents a significant advancement in securing IoMT environments through intelligent and adaptive intrusion detection.