A Review on Attack Landscape and Machine Learning Techniques for the Classification of Attacks in Internet of Medical Things (IoMT)

Abstract

ABSTRACT

Healthcare systems globally are struggling to handle the increasing number of patients, partly due to busy work schedules. To address this issue and enhance healthcare services, the Internet of Medical Things (IoMT) is gaining popularity. IoMT refers to internet-connected devices used in healthcare processes. However, the widespread adoption of IoMT devices has led to new security vulnerabilities and cyber threats. Protecting these devices from cyberattacks is vital for patient safety and data integrity. This study focuses on examining trends in cyber-attacks and the use of machine learning for attack classification in the Medical Internet of Things. The research involved a comprehensive analysis of relevant articles written in English between 2016 and 2023. The study established a search strategy and exclusion criteria to identify highly relevant works from reputable research databases. A significant number of papers were carefully chosen, organized, and reviewed. The reviewed articles delve into the threat landscape and assess the strengths and limitations of machine learning-based techniques for classifying security attacks in IoMT systems and networks. This study believes that this review can pave the way for the development of improved machine-learning methods for classifying attacks in the IoMT environment.