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Exploring the integration of blockchain technology, physical unclonable function, and machine learning for authentication in cyber-physical systems

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Abstract

In this rapidly advancing era, technology has been progressing extensively and swiftly. As a result, the emergence of numerous Cyber-Physical Systems (CPS) has become imperative to meet the technological demands of modern life. However, these systems generate a substantial amount of data, which poses challenges in terms of management, storage, and susceptibility to external attacks. This paper primarily focuses on the performance and security aspects of CPS, particularly in countering external threats, through the integration of blockchain technology and machine learning. It provides a comprehensive review of recent research findings that demonstrate the use of blockchain to enhance CPS performance while ensuring robust security. Furthermore, the paper explores the synergistic application of blockchain and machine learning techniques to reinforce CPS security. Moreover, it investigates how the combination of blockchain with physically unclonable functions (PUF) can significantly enhance the efficacy of physical device authentication.

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Authors and Affiliations

  1. Department of Information Technology, College of Computers and Information Technology, Taif University, 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, Bundoora, Australia

    Ben Soh

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  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. Exploring the integration of blockchain technology, physical unclonable function, and machine learning for authentication in cyber-physical systems. Multimed Tools Appl 83, 35629–35672 (2024). https://doi.org/10.1007/s11042-023-16979-2

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