Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

• Published in: Computers in biology and medicine Vol. 170; p. 108036
• Main Authors: Messinis, Sotirios, Temenos, Nikos, Protonotarios, Nicholas E., Rallis, Ioannis, Kalogeras, Dimitrios, Doulamis, Nikolaos
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2024; Elsevier Limited
• Subjects: Anomalies; Artificial Intelligence; Computer Security; Cryptography; Cybersecurity; Deep learning; Humans; Internet; Internet of Medical Things; IoMT security; Machine Learning; Networked medical devices; Privacy; Reproducibility of Results; Deep learning; Privacy; IoMT security; Internet of Medical Things; Machine learning; Cybersecurity; Networked medical devices; Artificial intelligence
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108036

Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare. •Systematically reviews the role of AI technologies, such as ML and DL, in enhancing IoMT security.•Presents several IoMT-related security and privacy attacks.•Highlights how the integration of AI improves the reliability and effectiveness of cybersecurity technologies in IoMT.•Proposes future research directions, focusing on AI’s role in patient data protection and data-driven healthcare within the IoMT framework.