Improving neighboring vehicle method to detect sybile attack in vehicular ad hoc network

Abstract

Recent technology known as Vehicular Ad Hoc Network (VANET) is invited to serve new vehicle driving experience. It is very useful to mitigate collision and utilizes traffic. Even though, VANET seems to be a promising technology, its drawbacks are inadequate with the security for a public accessible technology. VANET security is essential because a badly designed VANET is vulnerable to network attacks, and this may danger the safety of drivers, and As long as VANETs are the wireless network, there are different kinds of attacks and threats can happen in VANETs. Sybil attack is one of the most important attacks in VANETs. This thesis deals with the problem of the security in VANET especially in Sybil attack. In Sybil attack, a vehicle makes the identities of several vehicles; these IDs can be used for playing any kind of attack in the network. These false identities also create the illusion that there are additional vehicles on the road. In this research, a robust detection mechanism against Sybil attack in VANET is addressed based on fuzzy detection mechanism. Our contribution behind the implementation of proposed approach is that each vehicle has different set of neighbors providing sufficiently high density in VANET. In the simulation section, we study the proposed method in different ways and present the efficiency of the fuzzy method based on true and false detection rate and overall overhead.