Machine learning for authentication and authorization in IoT: taxonomy, challenges and future research direction

Ahmed, Kazi Istiaque and Tahir, Mohammad and Habaebi, Mohamed Hadi and Lau, Sian Lun and Ahad, Abdul (2021) Machine learning for authentication and authorization in IoT: taxonomy, challenges and future research direction. Sensors, 21 (15). pp. 1-34. E-ISSN 1424-8220

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Abstract

With the ongoing efforts for widespread Internet of Things (IoT) adoption, one of the key factors hindering the wide acceptance of IoT is security. Securing IoT networks such as the electric power grid or water supply systems has emerged as a major national and global priority. To address the security issue of IoT, several studies are being carried out that involve the use of, but are not limited to, blockchain, artificial intelligence, and edge/fog computing. Authentication and authorization are crucial aspects of the CIA triad to protect the network from malicious parties. However, existing authorization and authentication schemes are not sufficient for handling security, due to the scale of the IoT networks and the resource-constrained nature of devices. In order to overcome challenges due to various constraints of IoT networks, there is a significant interest in using machine learning techniques to assist in the authentication and authorization process for IoT. In this paper, recent advances in authentication and authorization techniques for IoT networks are reviewed. Based on the review, we present a taxonomy of authentication and authorization schemes in IoT focusing on machine learning-based schemes. Using the presented taxonomy, a thorough analysis is provided of the authentication and authorization (AA) security threats and challenges for IoT. Furthermore, various criteria to achieve a high degree of AA resiliency in IoT implementations to enhance IoT security are evaluated. Lastly, a detailed discussion on open issues, challenges, and future research directions is presented for enabling secure communication among IoT nodes.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Internet of Things; IoT; security; authentication; authorization; machine learning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	29 Jul 2021 09:58
Last Modified:	29 Jul 2021 09:58
URI:	http://irep.iium.edu.my/id/eprint/91123

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