Secure Connected and Automated Vehicles against False Data Injection Attack using Cloud-based Data Fusion

It has been shown that interdependency in connected and automated vehicles (CAV) can be potentially beneficial in several aspects, however, it also poses a set of specific challenges in concern of safety and reliability due to the possibility of cyber-attacks. In this paper, we present a data fusion-based methodology to detect the false data injection (FDI) attack on CAVs, and generate a flow of trustworthy information for every CAV. The effectiveness of the proposed approach is validated using microscopic traffic simulation, which shows that our proposed methodology is able to detect and isolate the false data injection attacks on CAVs.