An Efficient Detection and Mitigation of Selfish and Black Hole Attack in Cognitive Mobile Ad-hoc Network

Abstract

Cognitive radio network (CRN) is one where the licensed unused band can be redistributed among the demanding of users without an access to the licensed bands. It is essentially a programmable software radio. Such architecture suffers from different types of attacks, i.e. selfish attack and black hole attack. In this work, the problem is solved by providing an integrated solution for detecting selfish and black hole attacks and once the attack is detected that is being mitigated to all secondary user (SU), such that the SU would blacklist the attacking node. The result shows that. Such attack detection and mitigation improve the network quality significantly by allowing the node to black listed the attacking node and re-utilize of the spectrum among the among the SU nodes.