Open Access
ARTICLE
Authentication of Vehicles and Road Side Units in Intelligent Transportation System
Muhammad Waqas1, 2, Shanshan Tu1, 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.
Keywords
Cite This Article
APA Style
Waqas, M., Tu, S., Rehman, S.U., Halim, Z.,
Anwar, S. et al. (2020). Authentication of vehicles and road side units in intelligent transportation system. Computers, Materials & Continua, 64(1), 359-371. https://doi.org/10.32604/cmc.2020.09821
Vancouver Style
Waqas M, Tu S, Rehman SU, Halim Z,
Anwar S, Abbas G, et al. Authentication of vehicles and road side units in intelligent transportation system. Comput Mater Contin. 2020;64(1):359-371 https://doi.org/10.32604/cmc.2020.09821
IEEE Style
M. Waqas et al., "Authentication of Vehicles and Road Side Units in Intelligent Transportation System," Comput. Mater. Contin., vol. 64, no. 1, pp. 359-371. 2020. https://doi.org/10.32604/cmc.2020.09821
Citations