Distinguishing Sensor Faults and Attacks in Connected Vehicle Networks Using Two-Step Verification

As the reliance on vehicle-to-vehicle communication within transportation networks increases, the risk of cyber attacks is an escalating concern. In addition to attacks, faulty sensors within vehicles can also lead to transportation network failures. To address these challenges, this research proposes a two-step process for identifying and classifying malicious and faulty vehicles within a network using sequential reputation estimation and validation. The reputation estimation step calculates the cumulative trustworthiness of vehicles by validating their broadcasted Basic Safety Messages (BSMs) using the sensors of neighboring vehicles, while the reputation verification step classifies low reputation vehicles as faulty or attacked by assigning them additional tasks and analyzing their responses. Simulation results demonstrate the effectiveness of the proposed approach in accurately detecting and distinguishing faulty and malicious vehicles.