Detection & Prevention of Sybil attack using Artificial Bee Colony Algorithm in proximity of Closeness Centrality

Abstract

Vehicular ad-hoc networks (VANETs) are the wireless network in which vehicles are equipped with devices that communicates with each other via data packets. VANETs are mainly advocated for applications such as traffic collision detection, toll collection, controlling traffic congestion, weather forecasting, road diversion warning, car maintenance etc. Security of VANETs are vulnerable to various attacks such as Denial of Service (DoS), GPS Spoofing, Message Alteration, Black hole, Wormhole attack, Spamming attack, Node Impersonation attack, Sybil attack, Man-In-The-Middle attack etc. One of the most hazardous attacks is Sybil attack in which malicious vehicle mislead other vehicles by duplicating multiple identities and generating false information. The genuine vehicle believes this false information and hence leads to road accidents, traffic congestion, chaos etc. We have proposed a method to secure VANETs from Sybil attack using Artificial Bee Colony (ABC) Algorithm in proximity of closeness centrality. This algorithm optimizes node position by monitoring nodes and calculating shapely values. Thus enhancing network performance which is demonstrated in simulation results.