Evaluating trust models for improved event learning in VANETs

This paper studies the performance of two different trust models in a vehicular network scenario. Like all networks, messages in Vehicular Ad hoc Networks (VANETs) are prone to malicious tampering. Tampered messages render these networks useless for active safety applications. That is why trust models have been proposed in addition to the available authentication mechanisms. Identifying the most suitable model suitable for VANET scenarios is important for enhancing security of the these networks. For this reason the performance of two most promising models that consider the credibility of recommendations in trust computations, is studied in this paper. It is identified which of the two model provides most accurate identification of malicious nodes and true events happening in the network. The two models have been compared on the basis of True Positive Rate (TPR), False Positive Rate (FPR), Event Detection Probability (EDP), and Trust Computation Error (TCE).