Secure and collaborative spectrum sensing scheme based on audit game

Abstract

To defend against poisoning attacks and free-riding attacks conducted by malicious sensing terminals in crowd sensing-based collaborative spectrum sensing (CCSS), a novel audit game-based defense scheme was proposed, which combined the pre-deterrence and post-punishment mechanisms.Firstly, considering the audit budget constraint, a mixed-strategy audit game model under incomplete information was designed, which set a penalty strategy to deter malicious collaborators before spectrum sensing, and audited and punished them after the fusion of sensing data.Then, a lightweight audit chain model with on-chain and off-chain collaboration was designed, in which audit evidence was stored in an off-chain data warehouse and its metadata was publicly published on the blockchain.Furthermore, a distributed and intelligent audit algorithm based on reinforcement learning was devised to adaptively seek the approximate mix-strategy equilibrium of the audit game.Simulation results demonstrate that the proposed scheme can quickly obtain the stable and approximately optimal audit strategies and actively suppress the poisoning and free-riding behaviors of malicious collaborators, in comparison with conventional schemes.

Keywords

• collaborative spectrum sensing;audit game;security;blockchain;reinforcement learning