A Survey on Detection of Man-In-The-Middle Attack in IoMT Using Machine Learning Techniques

Digital world interconnectedness is increasing day by day. This has raised cybersecurity concerns, especially man-in-the-middle attacks which are considered very dangerous in the IoMT field. This is because these attacks manipulate patients’ data. Man-in-the-middle attacks exploit vulnerabilities in the communication channel. Malicious actors can intercept the data. So, one way is to use machine learning techniques to detect and predict these attacks. Also, use realistic and up-to-date datasets for training and evaluating detection methods. This paper presents a comprehensive review of the detection of MITM attacks using machine learning techniques with research gaps. The paper further investigates the challenges various MITM attacks pose, such as IP spoofing, DNS manipulation, and session hijacking, within the IoMT environment. It highlights the attack and defense mechanisms in countering ever-evolving man-in-the-middle attacks. The review also explores the implications of MITM attacks in IoMT.