An Intrusion Detection System for Internet of Medical Things

Abstract

The term IoMT (Internet of Medical Things) describes the connection of medical devices and software applications relating to healthcare information to the Internet using networking technologies. While these technologies bring the promise of improved patient care, improved efficiency, and reduced costs, they also bring new risks as many these connected devices are unmanaged and unprotected. This thesis focuses on providing a highly secure transmission of medical data in IoMT to ensure accuracy and confidentiality of patients’ data. We propose a novel intrusion detection system (IDS) based on machine learning (ML) methods which uses both network and biometric parameters as features and can differentiate the normal traffic from attack traffic. Experimental results indicate that our secured healthcare system can detect anomalies in both the network flow and patient’s biometric readings. Finally, we present a comparative summary of the proposed scheme with an existing scheme in terms of accuracy and execution time.