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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anand Singh Rajawat & S. B. Goyal & Pradeep Bedi & Tony Jan & Md Whaiduzzaman & Mukesh Prasad, 2023. "Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)," Future Internet, MDPI, vol. 15(8), pages 1-21, August.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:271-:d:1217488
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    References listed on IDEAS

    as
    1. Rajasekhar Chaganti & Azrour Mourade & Vinayakumar Ravi & Naga Vemprala & Amit Dua & Bharat Bhushan, 2022. "A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things," Sustainability, MDPI, vol. 14(19), pages 1-18, October.
    2. Sudhanshu Joshi & Manu Sharma & Rashmi Prava Das & Joanna Rosak-Szyrocka & Justyna Żywiołek & Kamalakanta Muduli & Mukesh Prasad, 2022. "Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    3. Amir Masoud Rahmani & Seyedeh Yasaman Hosseini Mirmahaleh, 2022. "Flexible-Clustering Based on Application Priority to Improve IoMT Efficiency and Dependability," Sustainability, MDPI, vol. 14(17), pages 1-31, August.
    Full references (including those not matched with items on IDEAS)

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