Sustenance against RL-Based Sybil Attacks in Cognitive Radio Networks Using Dynamic Reputation System

In this paper, we formulate novel threat and defense mechanisms for the Sybil attack problem in Cognitive Radio Networks (CRN). We present potential identity sampling strategies that a malicious Sybil attacker can use to enhance its attack capability and impact without being detected. We investigate how a Sybil attacker can leverage reinforced learning technique to improve its performance. We also formulate a novel dynamic reputation mechanism to defend against such threat that relies on the nodes' reporting in an intelligent and adaptive manner. Results obtained shows that a Sybil attacker can improve its performance using RL technique. It also demonstrates that the use of the dynamic reputation mechanism can considerably reduce the effectiveness of Sybil attacks and improve the accuracy of spectrum decisions.