Agreement-Induced Data Verification Model for Securing Vehicular Communication in Intelligent Transportation Systems

Abstract

Intelligent Transportation security requires cooperative credentials for sharing navigation and communication data between the vehicles. However due to the dynamic environment, communication is interrupted by the adversaries, resulting in non-privacy issues. This article introduces an Agreement-induced Data Verification Model (ADVM) for securing vehicular communication against adversaries. The connected vehicles in a grid communicate with each other based on direct and indirect recommendation. This recommendation is based on mutual identity sharing between the vehicles for masked information exchange. Non-replicated and recommendation based verifications are performed using the vector classification learning. In this learning process, the credential validity and communication tolerance amid adversaries are augmented. The constraint-failing vehicles are disconnected from the communication grid, preventing its insecure impact over the communication. The proposed model’s performance is verified using false rate, success ratio, processing time, complexity, and recommendation ratio. For the different vehicles, the proposed model achieves 9.69% less false rate, 10.3% success ratio, 10.49% less processing time, 10.3% less complexity, and 12.87% high recommendation ratio.