A Trusted Measurement Scheme for Connected Vehicles Based on Trust Classification and Trust Reverse

As security issues in vehicular networks continue to intensify, ensuring the trustworthiness of message exchanges among vehicles, infrastructure, and cloud platforms has become increasingly critical. Although trust authentication serves as a fundamental solution to this challenge, existing models fail to effectively address the specific requirements of vehicular networks, particularly in defending against malicious evaluations. This paper proposes a novel multidimensional trust evaluation framework that integrates both static and dynamic metrics. To tackle the issue of malicious ratings in peer assessments, a rating reversal mechanism based on K-means clustering is designed to effectively identify and correct abnormal trust feedback. In addition, the framework incorporates an entropy-based trust weight allocation mechanism and a time decay model to enhance adaptability in dynamic environments. The simulation results demonstrate that, compared with traditional approaches, the proposed scheme improves the average successful information rate by 12% and reduces the false positive rate to 6.1%, confirming its superior performance in securing communications within the vehicular network ecosystem.