Distinguishing Sensor Faults and Malicious Attacks in Connected Vehicles Using Machine Learning

Connected and Autonomous Vehicles (CAVs) enhance the safety and efficiency of transportation by communicating sensor data through Basic Safety Messages (BSMs). This coordinated navigation strategy is vulnerable to both unavoidable sensor faults and intentional malicious attacks. Confusing sensor faults and malicious attacks can lead to inappropriate responses that compromise the safety of passengers and infrastructure. This work addresses this issue by first systematically introducing both simulated faults (drift, hard-over) and malicious attacks (colluding and replay) into the publicly-available Tampa CV Pilot BSM dataset. We then evaluate the ability of various classical machine learning approaches and ensemble classifiers like Random Forest (RF) to automatically detect anomalies and distinguish between sensor faults from malicious attacks.