An Empirical Study of Machine Learning Techniques for Authentication in Vehicle to Vehicle Communication

Vehicle-to-Vehicle (V2V) communication is essential for maintaining road safety, effective traffic management, and the advancement of intelligent transportation systems (ITS) in the era of linked and autonomous cars. Secure authentication is essential in vehicle-to-vehicle (V2V) communication, though, because cars share private data including trajectory, speed, and location. This assessment offers a thorough analysis of the state-of-the-art authentication methods, their difficulties, and the most recent developments in V2V communication security. In this paper, we outline future research directions and concentrate on machine learning, deep learning, and reinforcement learning approaches for V2V authentication.