@Article{cmc.2020.09821,
AUTHOR = {Muhammad Waqas, Shanshan Tu, Sadaqat Ur Rehman, Zahid Halim, Sajid 
Anwar, Ghulam Abbas, Ziaul Haq Abbas, Obaid Ur Rehman},
TITLE = {Authentication of Vehicles and Road Side Units in Intelligent Transportation System},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {359--371},
URL = {http://www.techscience.com/cmc/v64n1/39147},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.09821}
}