Sybil attack detection in VAVET by using neighbouring vehicles in Wireless Sensor Networks

COMPUTER ENGINEERING

Abstract - Wireless Sensor Networks play a major role in revolutionizing the world by its sensing technology. Wireless Sensor Networks (WSNs) has emerged as that powerful technology which has multiple applications such as such as military operations, surveillance system and Intelligent Transport Systems (ITS). One severe attack is Sybil attack, in which a malicious node forges large number of fake identities in order to disrupt the proper functioning of VANET applications. Fake information reported by a single malicious vehicle may not be highly convincing because most of the VANET applications require several vehicles to reinforce a particular information before accepting as a truth. A Sybil attacker pretends multiple vehicles in order to reinforce false messages. . In the recent times, various techniques have been proposed for the detection of malicious node from the network. The proposed techniques is based on monitor mode and distance based techniques. The vehicles and the elements that are present at the roadside are connected to each other for the purpose of communication and this network is self-configuring in nature, this is the reason that communication can be done inefficient manner through the network. Motivation behind the design of proposed approach is to locate Sybil nodes quickly without using secret information exchange and special hardware. The simulation is been performed in NS2. NS2 is an open – source simulation tool running on Unix – like operating systems. In our research, straightforwardly study the impact of parameters such as wireless communication range, vehicular densities, distance between source the destination, and minimum and maximum vehicle speeds on the end-to-end delay. The results shows that purposed technique shows good results in terms of various parameter. Here some perimeters are packet loss, throughput and routing overhead. Extensive simulation results demonstrate the accuracy of our analysis.

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