A Machine and Deep Learning Approaches for Intrusion Detection and Attack Classification in Medical IoT Networks

Abstract

In the domain of the Internet of Medical Things (IoMT), upholding the confidentiality, integrity, and availability of sensitive medical data is of utmost importance. However, the intricate network of inter connected IoMT devices presents formidable challenges in effectively identifying intrusions and categorizing attacks. This research project focuses on harnessing the capabilities of both machine learning and deep learning techniques to develop robust systems for intrusion detection and attack classification, specifically tailored to IoMT environments. Through the implementation of cutting-edge algorithms and methodologies, our objective is to strengthen the security framework of IoMT systems, thus ensuring the protection of patient data against unauthorized access, tampering, and disruptions in service. By conducting thorough experimentation and analysis, we aim to assess the performance of various models and methodologies, with the ultimate aim of achieving high levels of detection accuracy while minimizing false positives and false negatives. Ultimately, our research endeavors to drive forward progress in IoMT security, contributing to the creation of safer and more dependable healthcare delivery systems.