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Effective applications of vehicular ad hoc networks in traffic signal controlrequire new methods for detection of malicious data. Injection of maliciousdata can result in significantly decreased performance of such applications,increased vehicle delays, fuel consumption, congestion, or even safety threats.This paper introduces a method, which combines a model of expected driverbehaviour with position verification in order to detect the malicious datainjected by vehicle nodes that perform Sybil attacks. Effectiveness of thisapproach was demonstrated in simulation experiments for a decentralizedself-organizing system that controls the traffic signals at multipleintersections in an urban road network. Experimental results show that theproposed method is useful for mitigating the negative impact of malicious dataon the performance of traffic signal control.

Detection of malicious data in vehicular ad-hoc networks for traffic signal control applications