Cybersecurity of Inference in Vehicular Ad-hoc Networks

In recent years, there has been a surge in research and development efforts on vehicular ad-hoc networks (VANETs) with the objective to make driving safer. VANETS that share sensor data can provide tremendous improvements in this respect. Unfortunately, such VANETs are known for numerous security concerns and are vulnerable to cyber-attacks. In this paper we focus on studying cyber physical attacks on VANETs which share sensor data among vehicles to track important objects, an important emerging topic that has received little attention. We develop an appropriate VANET system model along with attack detection methods that can find any attack that impacts tracking of important objects like other vehicles or pedestrians regardless of how the attack is launched. This includes attacks modifying hardware, software, sensor data, communications or anything else. We have not seen any similar work. We illustrate these ideas with numerical results for a specific efficient distributed tracking algorithm. We describe a attack detection algorithm and numerically investigate the performance.