An Efficient Sybil Attack Detection Approach for Vehicular Ad-Hoc Networks

Vehicular ad-hoc networks (VANETs) play a vital role in next-generation wireless communication. It consists of a set of moving vehicles, stationary vehicles, and roadside units (RSUs) that are connected through a wireless network. However, security and privacy issues are always challenging in VANETs as they lead to attacks. One such attack is the detection of the Sybil attack. This attack makes use of a vehicle or node to create multiple fake identities, at a time, to take advantage of a particular network. This attack leads to traffic congestion, rerouting, and accident. Many researchers have proposed solutions to identify the Sybil attack. Recently, Ayaida et al. [1] have presented an approach to detect the Sybil attack based on a macroscopic traffic flow model. This approach calculates the estimated speed and real speed, and their difference. If the difference is more than a predetermined threshold, then it suspects an attack and sends a message to nearby vehicles. This paper modifies their approach to provide a better solution by estimating the speed of vehicles and by RSU, thereby averaging their speed. The proposed approach is simulated using network simulator (ns) 3 and simulation of urban mobility (SUMO) to show its effectiveness.