Development of Machine Learning Model to Detect Fault Data Injection Attack on Vehicle Following Systems

A Vehicle-following system is a system that controls a following vehicle to maintain a fixed distance and acceleration behind a preceding vehicle. Cyber attack is an issue on Vehicle following systems due to its wireless connectivity. Cyber dangers to physical systems result from this. As a result, controlling the action of the vehicle is necessary to stop the cyber attack, which causes the development of cyber physical system(CPS). CPS can be accessed and controlled remotely. Their features make them more easily vulnerable to cyber attacks. Fault Data Injection (FDI) attack is an attack that is mathematically fed into the altered Vehicle Following System and modeled to study the attack's effects. The Scope of this work is to develop a machine learning model by generating the cyberattack dataset by analyzing the Physical Model of Vehicle following system. Decision tree algorithm is used to detect the attack. It considerably raises FDI detection accuracy.