Open Access

ARTICLE

Authentication of Vehicles and Road Side Units in Intelligent Transportation System

Muhammad Waqas^{1, 2}, Shanshan Tu^{1, 3, *}, Sadaqat Ur Rehman¹, Zahid Halim², Sajid Anwar², Ghulam Abbas², Ziaul Haq Abbas⁴, Obaid Ur Rehman⁵

1 Beijing Key Laboratory of Trusted Computing, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
2 Faculty of Computer Science & Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences & Technology, Topi, 23460, Pakistan.
3 Beijing Electro-Meahnical Engineering Institute, Beijing, 100074, China.
4 Faculty of Electrical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences & Technology, Topi, 23460, Pakistan.
5 Department of Electrical Engineering, Sarhad University of Science and Information Technology, Peshawar, 25000, Pakistan.

* Corresponding Author: Shanshan Tu. Email: .

Computers, Materials & Continua 2020, 64(1), 359-371. https://doi.org/10.32604/cmc.2020.09821

Received 20 January 2020; Accepted 31 March 2020; Issue published 20 May 2020

Abstract

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents, economically damaging traffic jams, hijacking, motivating to wrong routes, and financial losses for businesses and governments. Smart and autonomous vehicles are connected wirelessly, which are more attracted for attackers due to the open nature of wireless communication. One of the problems is the rogue attack, in which the attacker pretends to be a legitimate user or access point by utilizing fake identity. To figure out the problem of a rogue attack, we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link. We consider the communication link between vehicle-tovehicle, and vehicle-to-infrastructure. We evaluate the performance of our proposed technique by measuring the rogue attack probability, false alarm rate (FAR), misdetection rate (MDR), and utility function of a receiver based on the test threshold values of reinforcement learning algorithm. The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.

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Citations