A Lightweight Mutual Authentication and Privacy-preservation Scheme for Intelligent Wearable Devices in Industrial-CPS.

Autor: Jan MA; Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan., Khan F; Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan., Khan R; Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan., Mastorakis S; Computer Science Department, University of Nebraska Omaha, NE, USA 68182-0002., Menon VG; Computer Science Engineering Department, at SCMS Group of Educational Institutions, India., Alazab M; Charles Darwin University 59, Chataway Cr Casuarina, NT, AUS 0811., Watters P; School of Engineering and Mathematical Sciences, La Trobe University Melbourne, VIC, AUS 3086.
Jazyk: angličtina
Zdroj: IEEE transactions on industrial informatics [IEEE Trans Industr Inform] 2021 Aug; Vol. 17 (8), pp. 5829-5839. Date of Electronic Publication: 2020 Dec 10.
DOI: 10.1109/tii.2020.3043802
Abstrakt: Industry 5.0 is the digitalization, automation and data exchange of industrial processes that involve artificial intelligence, Industrial Internet of Things (IIoT), and Industrial Cyber-Physical Systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with the emergence of innovative healthcare applications. Similar to any other IIoT paradigm, I-CPS capable healthcare applications face numerous challenging issues. The resource-constrained nature of wearable devices and their inability to support complex security mechanisms provide an ideal platform to malevolent entities for launching attacks. To preserve the privacy of wearable devices and their data in an I-CPS environment, we propose a lightweight mutual authentication scheme. Our scheme is based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities. After mutual authentication, the privacy risk associated with a patient data is predicted using an AI-enabled Hidden Markov Model (HMM). We analyzed the robustness and security of our scheme using BurrowsAbadiNeedham (BAN) logic. This analysis shows that the use of lightweight security primitives for the exchange of session keys makes the proposed scheme highly resilient in terms of security, efficiency, and robustness. Finally, the proposed scheme incurs nominal overhead in terms of processing, communication and storage and is capable to combat a wide range of adversarial threats.
Databáze: MEDLINE