Investigating the security issues of multi-layer IoMT attacks using machine learning techniques

The Internet of Medical Things (IoMT) plays a significant role in the healthcare system as it improves effectiveness and efficiency of treatment by continuously monitoring patients using smart home sensor and wearables (Fig. 1), early disease diagnosis using data collected from the Internet of Medical Things (IoMT) devices and assisting doctors in deciding the best treatment and acting immediately if necessary. Additionally, it helps to reduce the number of hospital visits, limiting carbon footprint.IoMT devices are vulnerable to Multi-layer attacks because most of these devices are resource-constrained and portable, which is why there is not that much implementation of security features in these devices and making them a prime target for intruders looking to steal patients’ sensitive information and healthcare records. Multi-layer attacks are a group of attacksexploiting multiple layers of IoMT architecture. Denial-of-service (DoS) and Man-In-The-Middle (MITM) attacks, for instance, can target the three layers of the IoMT system and lead to serious consequences, such as theft of patients’ sensitive data and reputational damages. The main aim of the project is to create a robust IDS for IoT devices.

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