Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

Anand Singh Rajawat, S. B. Goyal, Pradeep Bedi, Tony Jan, Md Whaiduzzaman and Mukesh Prasad ()
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Anand Singh Rajawat: School of Computer Sciences & Engineering, Sandip University, Nashik 422213, India
S. B. Goyal: Faculty of Information Technology, City University, Petaling Jaya 46100, Malaysia
Pradeep Bedi: School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India
Tony Jan: Centre for Artificial Intelligence Research and Optimization, Design and Creative Technology Vertical, Torrens University, Sydney 2007, Australia
Md Whaiduzzaman: School of Information Technology, Torrens University, Brisbane 4006, Australia
Mukesh Prasad: School of Computer Science, Faculty of Engineering and IT (FEIT), University of Technology Sydney, Sydney 2007, Australia

Future Internet, 2023, vol. 15, issue 8, 1-21

Abstract: Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications.

Keywords: vulnerability prediction; Internet of Things; quantum machine learning; Internet of Medical Things (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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