Vehicle-to-Everything Trust: Enabling Autonomous Trust Assessment of V2X Data by Vehicles
Connected and automated vehicles rely on data from various entities to support safety-critical applications such as Cooperative Adaptive Cruise Control (CACC). However, unauthorized data manipulation through, for example, data injection attacks can compromise vehicle safety and lead to incidents. Existing vehicular security mechanisms, such as Misbehavior Detection System (MBD), have limitations in detecting and mitigating all types of threats on their own. To address these limitations, our prior work has proposed the concept of a Trust Assessment Framework (TAF), which assesses data trustworthiness by combining evidence from multiple security systems operating as trust sources. However, TAF as a concept has not been extensively evaluated in safety-critical Cooperative Driving (CD) applications. In this work, we refine the architecture of the TAF and implement a software prototype based on it. We integrate the TAF prototype with a CACC simulation environment and implement three types of data injection attacks. We demonstrate that by incorporating multiple security mechanisms as trust sources, the TAF significantly improves attack detection performance and reduces the number of crashes by 86% compared to using a single security mechanism, such as MBD.