CAMSI: Context-Aware Multi-Source Infrastructure-based Trust Model for Cooperative ITSs

Vehicular Ad hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITSs) and Traffic Information Systems (TISs). However, the open nature of wireless communication exposes these networks to security threats from compromised nodes. In this work, we propose a trust-based traffic information system to enhance the security of vehicular networks. We introduce an infrastructure-based trust management model utilizing Smart Road Signs (SRSs) to address scalability challenges posed by the limited computational and storage capabilities of smaller devices such as connected vehicles and smartphones. By enabling multi-source data aggregation, we mitigate single-source failure risks and improve environmental visibility. To enhance the accuracy of trust evaluation, we propose a two-level trust evaluation process, combining Bayesian Inference and dynamic weighted summation. Additionally, a probabilistic graph is used to estimate prior information for Bayesian inference, learning causal relationships from previous traffic incident reports. Simulation results demonstrate the effectiveness of our model in detecting malicious attacks, including false injection and on-off attacks, even in environments with up to 40% malicious nodes.