A network in which the vehicular nodes are free to join or leave the network is known as vehicular ad hoc network (VANET). Either vehicle to vehicle or vehicle to infrastructure types of communication is performed in this decentralized type of network. The identification and elimination of Distributed-Denial of Service (DDoS) attacks from VANETs is the major objective of this research. The nodes that can flood victim nodes with large numbers of rough packets are chosen by the malicious nodes in this kind of attack. Identifying such malicious nodes from the network is an important research objective to be achieved. The technique which is proposed in this research is based on the two step verification. In the two steps verification technique, when the network performance is reduced to threshold value then the traffic is monitored that which node is sending data on such high rate. NS2 simulator is used to implement the proposed technique. With respect to various performance parameters, the proposed technique is analyzed. A comparative evaluation of results achieved from proposed and existing techniques is also done to conclude the level of improvement achieved.

The Internet is a communication network where two or more than two users communicate and exchange the data. Black hole attack is a security threat in which a malicious node drops some or all of the packets. The proposed framework implements a biometric authentication system into the communication network to verify the user and to save the user from any internal or external threat. The main objective is to integrate the biometric security with the communication network. The attack is supposed to be a Black hole which has been considered as a smart attack. Feature extraction of Fingerprint dataset will be done using minutiae extractor. This will extract ridge endings and ridge bifurcation from the thinned image. Genetic algorithm is usedto reduce the features to useful pool. If the user is authentic only then prevention mechanism against black hole is applied. Genetic Algorithm is used to find out black hole node based on the fitness function. Proposed model’s performance is evaluated using various metrics like delay, throughput, energy consumption and packet delivery ratio.