A Malware Attack Enabled an Online Energy Strategy for Dynamic Wireless EVs within Transportation Systems

Abstract

Developing transportation systems (TSs) under the structure of a wireless sensor network (WSN) along with great preponderance can be an Achilles’ heel from the standpoint of cyber-attacks, which is worthy of attention. Hence, a crucial security concern facing WSNs embedded in electrical vehicles (EVs) is malware attacks. With this in mind, this paper addressed a cyber-detection method based on the offense–defense game model to ward off malware attacks on smart EVs developed by a wireless sensor for receiving data in order to control the traffic flow within TSs. This method is inspired by the integrated Nash equilibrium result in the game and can detect the probability of launching malware into the WSN-based EV technology. For effective realization, modeling the malware attacks in conformity with EVs was discussed. This type of attack can inflict untraceable detriments on TSs by moving EVs out of their optimal paths for which the EVs’ power consumption tends toward ascending thanks to the increasing traffic flow density. In view of this, the present paper proposed an effective traffic-flow density-based dynamic model for EVs within transportation systems. Additionally, on account of the uncertain power consumption of EVs, an uncertainty-based UT function was presented to model its effects on the traffic flow. It was inferred from the results that there is a relationship between the power consumption and traffic flow for the existence of malware attacks. Additionally, the results revealed the importance of repressing malware attacks on TSs.