Ensuring patient safety in IoMT: A systematic literature review of behavior-based intrusion detection systems

Integrating Internet of Medical Things (IoMT) devices into healthcare has enhanced patient care, enabling real-time data exchange and remote monitoring, yet it also presents substantial security risks. Addressing these risks requires robust Intrusion Detection Systems (IDS). While existing studies target this topic, a systematic literature review focused on the current state and advancements in Behavior-based Intrusion Detection Systems for IoMT environments is necessary. This systematic literature review analyzes 81 studies from the past five years, answering three key research questions: (1) What are the Behavior-based IDS currently used in healthcare? (2) How do the detected attacks impact patient safety? (3) Do these IDS include prevention measures? The findings indicate that nearly 84% of the reviewed studies utilize Artificial Intelligence (AI) techniques for threat detection. However, significant challenges persist, such as the scarcity of IoMT-specific datasets, limited focus on patient safety, and the absence of comprehensive prevention and mitigation strategies. This review highlights the need for more robust, patient-centric security solutions. In particular, developing IoMT-specific datasets and enhancing defensive mechanisms are essential to meet the unique security requirements of IoMT environments. • We present a systematic literature review of 81 primary studies. • AI techniques are widely adopted for detecting cybersecurity attacks within IoMT environments. • There is a lack of IoMT-specific datasets for effective attack detection. • Most studies overlook direct protection of patient safety. • Few studies implement effective prevention and mitigation measures.