Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

Abstract

Internet of Medical Things (IoMT) represents an application of the Internet of Things, where health professionals perform remote analysis of physiological data collected using sensors that are associated with patients, allowing real-time and permanent monitoring of the patient's health condition and the detection of possible diseases at an early stage. However, the use of wireless communication for data transfer exposes this data to cyberattacks, and the sensitive and private nature of this data may represent a prime interest for attackers. The use of traditional security methods on equipment that is limited in terms of storage and computing capacity is ineffective. In this context, we have performed a comprehensive survey to investigate the use of the intrusion detection system based on machine learning (ML) for IoMT security. We presented the generic three-layer architecture of IoMT, the security requirement of IoMT security. We review the various threats that can affect IoMT security and identify the advantage, disadvantages, methods, and datasets used in each solution based on ML. Then we provide some challenges and limitations of applying ML on each layer of IoMT, which can serve as direction for future study.

Flexible-Clustering Based on Application Priority to Improve IoMT Efficiency and Dependability

“…The study can only support the privacy-preserving of the related applications to ECG signals for providing proper service to a requester. Some case studies utilized the sparse learning method for the cryptography algorithm's decryption phase and improved the privacy of IoMT-based applications for increasing the accuracy of decrypted ECG signals, which only covered the related application to Electrocardiogram [21,22].…”