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Ensuring authentication in Medical Cyber-Physical Systems: A comprehensive literature review of blockchain technology integration with machine learning

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Abstract

Medical Cyber-Physical Systems (MCPS) are increasingly prevalent in an era of rapid technological advancement. They hold significant importance for patients and healthcare professionals, as they leverage cyber-physical systems (CPS). These systems generate substantial amounts of big data, posing challenges for processing and storage. Furthermore, they face vulnerabilities to cyber-attacks. With multiple parties involved, concerns arise regarding authentication processes and safeguarding against unauthorized access. To address these issues, Blockchain technology is employed to enhance system authentication and protect medical data. The integration of Blockchain and machine learning further improves the efficiency of big data processing in MCPS. This paper provides a comprehensive review of recent studies focusing on the integration of Blockchain technology with Medical Cyber-Physical Systems (MCPS) and its application in the medical field. The reviewed studies shed light on various aspects of utilizing Blockchain to enhance the security and efficiency of MCPS and protect medical data. Through these studies, several unresolved weaknesses, concerns and proposed solutions have been identified in the integration of Blockchain with Medical Cyber-Physical Systems (MCPS).

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Authors and Affiliations

  1. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, 21944, Taif, Saudi Arabia

    Hind A. Al-Ghuraybi & Mohammed A. AlZain

  2. Department of Computer Science and Computer Engineering, La Trobe University, Bundora, Australia

    Ben Soh

Authors
  1. Hind A. Al-Ghuraybi
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  2. Mohammed A. AlZain
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  3. Ben Soh
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Correspondence to Hind A. Al-Ghuraybi.

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Al-Ghuraybi, H.A., AlZain, M.A. & Soh, B. Ensuring authentication in Medical Cyber-Physical Systems: A comprehensive literature review of blockchain technology integration with machine learning. Multimed Tools Appl 83, 35673–35707 (2024). https://doi.org/10.1007/s11042-023-17065-3

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  • DOI: https://doi.org/10.1007/s11042-023-17065-3

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