Modeling Cyber Attacks at Intelligent Traffic Signals

Abstract

Transportation networks are considered one of the critical physical infrastructures for resilient cities (cyber-physical systems). In efforts to minimize adverse effects that come with the advancement of vehicular technologies, various governmental agencies, such as the U.S. Department of Homeland Security and the National Highway Traffic Safety Administration (NHTSA), work together. This paper develops belief-network-based attack modeling at signalized traffic networks under connected vehicle and intelligent signals frameworks. For different types of cyber attacks, defined in the literature, risk areas and impacts of attacks are evaluated. Vulnerability scores, technically based on the selected metrics, are calculated for signal controllers. In addition, the effect of having redundant traffic sensing systems on intersection performance measures is demonstrated in terms of average queue length differences.