Detection of False Data Injection Attack in Connected and Automated Vehicles via Cloud-Based Sandboxing

Abstract

In recent years, developments in vehicle-to-everything communication (V2X) have steadily increased in applications such as platooning and collision avoidance. V2X provides vehicles with long-range information regarding traffic congestion and routing, but also short and mid-range information allowing cooperative adaptive cruise control, automatic collision warnings, and others. Despite being potentially beneficial in several aspects, such interdependence poses a set of specific challenges from safety and reliability standpoint due to the possibility of cyber-attacks aimed at influencing the behavior of the vehicles. In this paper, a Cloud-based approach, a sandboxing framework is presented to detect the false data injection attack on connected and automated vehicles (CAVs). The sandboxing concept comes from computer security and it is adopted in a control framework as a way to isolate and evaluate the data exchanged by the CAVs affecting the vehicle control system. Numerical experiments are conducted to show the effectiveness of the approach using microscopic traffic simulation. Simulation results show that our proposed methodology is able to detect the false data injection attack existing in the V2X communication and identify the attacker in real-time less than 0.2 s.