A Fast Method to Defend Against SSDF Attacks in the CIoV Network: Based on DAG Blockchain and Evolutionary Game

This letter investigates the problem for Spectrum Sensing Data Falsification (SSDF) attacks in the Cognitive Internet of Vehicles (CIoV) network. The high-speed movement of Vehicle Users (VUs) leads to rapid changes in Channel State Information (CSI) and location. This leads to unstable detection probabilities and unstable probabilities of reporting errors. These unstable probabilities increase the error rate of traditional methods to identify VUs as Malicious Vehicle Users (MVUs). And high-speed movement makes it difficult to detect MVUs, which may result in massive MVUs' attacks. To address the above problems, this letter establishes the Cooperative Spectrum Sensing (CSS) and spectrum access process under a Directed Acyclic Graph (DAG) blockchain framework, models MVUs' attack strategy selection which is determined by revenue as an evolutionary game, and proposes a smart contract that changes the mining difficulty of VUs based on the correctness of local spectrum sensing decisions to influence VUs' revenue. Finally, the simulation results verify the theoretical analysis and prove that the proposed method is superior to the traditional method.