Intrusion Detection in Internet of Medical Things Using Digital Twins—A Review

Abstract

The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time data collection, analysis, and personalized treatment through interconnected devices such as sensors and wearables. The integration of Digital Twins (DTs), the virtual replicas of physical components and processes, has also been found to be a game changer for the ever-evolving IoMT. However, these advancements in the healthcare domain come with significant cybersecurity challenges, exposing it to malicious attacks and several security threats. Intrusion Detection Systems (IDSs) serve as a critical defense mechanism, yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks. With this context, this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain. We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025, aligned with the theme “Digital Twins and IDS in IoMT.” It was found that by simulating device behaviours and network interactions in IoMT, DTs not only provide a proactive platform for early threat detection, but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT. Overall, this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.