The advancements in Vehicular Networks communication technologies facilitate the exchange and sharing of traffic information,thereby significantly enhancing travel efficiency.However,the openness of V2X networks increases the vulnerability of traffic entities to attacks from malicious vehicles,potentially leading to severe consequences.Addressing this issue,this paper proposed a reputation evaluation method for vehicle nodes in V2X.Initially,a partitioned blockchain network for vehicle nodes in Vehicular Networks was introduced.Subsequently,local reputation values were calculated based on trust among vehicle nodes and auxiliary trust from infrastructures,combined with the use of deep learning for dynamically computing global reputation values.This enabled the identification of optimal data sharing nodes based on global reputation scores.Finally,to enhance storage technology,partitioned blockchain technology was employed to ensure the integrity and traceability of reputation values and traffic information.Simulation results demonstrated that the proposed method outperformed comparative methods in accurately identifying malicious nodes,as evidenced by higher precision and recall rates.
维普
万方数据