Feature efficiency in IoMT security: A comprehensive framework for threat detection with DNN and ML.

Background: To address critical security challenges in the Internet of Medical Things (IoMT), this study develops a feature selection framework to improve detection accuracy and computational efficiency in IoMT cybersecurity. By optimizing feature selection, the framework aims to enhance the security and operational integrity of real-time healthcare systems.

Method: This study integrates Random Subset Feature Selection (RSFS) with Correlation Feature Selection (CFS) to create a novel feature selection framework tailored to IoMT datasets.