A novel scheme to detect and remove black hole attack in cognitive radio vehicular ad hoc networks(CR-VANETs)

Now a days the rapid advancement of wireless communication, Cognitive Radio for vehicular Ad Hoc networks has received immense attention from researchers. Due to the ever increasing demand for more radio spectrum, Cognitive Radio Networks is a one of the solution for this crisis. Security is the one of the basic issue to implement a better CRN. CRN is established in a open communication environment that's why CRN are more vulnerable to security threats than wired networks. Black hole attack is one of the most common attack in CR-VANETs. If the Ad-hoc networks are affected by this attack, it not able to perform effectively. In this situation as a consequence the process of any event detection severely affected. The affected nodes can lead to discrepancies in data analysis and hence come with an erroneous report. We have proposed a simple scheme for recognition of malicious node in Black Hole attack in CR-VANET. In our proposed model, we used a Trusty Dynamic Software Agent (TDSA) which are employed for each node in VANETs and shares databases in the memory spaces of the neighboring nodes. We have compared the communication range(R) and distance between two adjacency nodes. Thus we can detect the connectivity of the two nodes and decide the actual alternate path. After recognition of malicious node we change the effected route and an alternate pathway have proposed. and thus we can eliminate black hole attack from CR-VANETs.