TY - JOUR
T1 - Lightweight Mutual Authentication and Privacy-Preservation Scheme for Intelligent Wearable Devices in Industrial-CPS
AU - Jan, Mian Ahmad
AU - Khan, Fazlullah
AU - Khan, Rahim
AU - Mastorakis, Spyridon
AU - Menon, Varun G.
AU - Alazab, Mamoun
AU - Watters, Paul
N1 - Funding Information:
Manuscript received August 21, 2020; revised November 5, 2020; accepted December 1, 2020. Date of publication December 10, 2020; date of current version May 3, 2021. This work was supported by a pilot award from the Center for Research in Human Movement Variability and the NIH under Grant P20GM109090 and a planning award from the Collaboration Initiative of the University of Nebraska system. Paper no. TII-20-4001. (Corresponding author: Fazlullah Khan.) Mian Ahmad Jan and Rahim Khan are with the Department of Computer Science, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan.
Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - 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, in this article 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. We analyzed the robustness and security of our scheme using Burrows-Abadi-Needham 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.
AB - 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, in this article 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. We analyzed the robustness and security of our scheme using Burrows-Abadi-Needham 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.
KW - Artificial intelligence (AI)
KW - Industrial Internet of Things (IIoT)
KW - authentication
KW - client-server model
KW - industrial cyber-physical systems (I-CPS)
KW - privacy
KW - security
UR - http://www.scopus.com/inward/record.url?scp=85097943880&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097943880&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.3043802
DO - 10.1109/TII.2020.3043802
M3 - Article
AN - SCOPUS:85097943880
VL - 17
SP - 5829
EP - 5839
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
IS - 8
M1 - 9290438
ER -