Protecting the Internet of Vehicles Against Advanced Persistent Threats: A Bayesian Stackelberg Game
Talal Halabi, Omar Abdel Wahab, Ranwa Al Mallah, Mohammad Zulkernine
Abstract
Connected vehicles are essential for the deployment of intelligent transportation services. However, the high level of connectivity in today's Internet of vehicles (IoV) and the extreme reliance on the data collected from the smart transportation infrastructure widen the space of security vulnerabilities, making the IoV a potential target for cyberattacks. This article investigates novel sophisticated ways to exploit the IoV and launch intelligent attacks on road traffic services by creating persistent impact and reducing detection chances. This article models the processes of attack and defense as a cybersecurity Stackelberg game leading to optimal mixed strategies for both the attackers and the IoV defense system, where the latter optimally deploys the available security resources within the transportation infrastructure to minimize the impact of attacks and improve their detection. The game is of Bayesian type and considers several types of data corruption attacks that occur according to a probability distribution that we determine based on a rigorous risk assessment approach. The results show that our game model and solution allow us to reduce the impact of advanced persistent threats compared to a uniform defense design that is indifferent to attackers' strategies and types. The solution could be integrated into the design of IoV intrusion detection systems to increase their robustness.