Short paper: Experimental analysis of misbehavior detection and prevention in VANETs

Abstract

Vehicular Ad-hoc Networks (VANETs) aim to increase, among others, traffic safety and efficiency by warning and informing the driver about road events and hazards. Due to their direct impact on drivers' safety, external and internal attacks have to be prevented. While authentication prevents most of the external attacks, internal attackers are still able to misuse the system and inject fake - but authenticated - messages. Therefore, misbehavior detection and prevention mechanisms are required to mitigate such attacks. In this paper we provide a categorization of internal attackers to identify most relevant attack variants. Instead of using simulations, as done by most related works, we use an implementation on real vehicles to demonstrate the feasibility of location-based attacks. Especially, we demonstrate that a malware application installed on a vehicle can provoke false warnings on benign vehicles that are within the attacker's communication range. This exemplary attack is possible due to insufficiently specified VANET security standards. By using our proposed countermeasures, we show that this internal attack is detected and blocked, preventing false driver warnings.