VANETs are susceptible to safety threats due to cumulative dependence upon transmission, computing, and control mechanisms. Therefore, securing the end to end communication in VANETs becomes a major area of research. Many researchers have proposed several security protocols so far to improve the integrity, confidentiality, nonrepudiation, access control, etc. to provide secure VANETs to its users. Therefore, the overall goals of security protocols of VANETs are to recognize malicious nodes in the network by using suitable mechanism. In this work trustworthiness of VANETs has been improved by using some well-known adaptive Neuro-fuzzy system tools to detect the attacks in more efficient manner. Adaptive Neuro-fuzzy system tools have been used frequently to monitor the behavior of VANETs nodes and evaluate some malicious nodes based upon already developed model using historical knowledge of the same network. Since, training of the model is based upon the various features of VANETs nodes therefore, it is able to monitor the attack even in complex environment. Extensive analysis indicates that the proposed protocol outperforms others in terms of Packet Loss Ratio, Throughput, End to End Delay and Average Download Delay.

VANET, Adaptive neuro-fuzzy system, Attacks, Malicious nodes

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