Comparative Study of Misbehavior Detection System for Classifying misbehaviors on VANET
Omessaad Slama, Bechir Alaya, Salah Zidi, Mounira Tarhouni · 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT) · 2022
The purpose of the research on Vehicular Ad hoc NETwork (VANET) is to improve road safety, provide passenger comfort, and prevent accidents. Messages sent through VANETs are vulnerable to a variety of misbehaviors. Traditional techniques, like encryption, are ineffective since there is no purpose in being impervious to insider misbehavior. In this study, we propose an automatic learning method for detecting the misbehaving message deliver through a vehicle in VANETs using machine learning and deep learning techniques. To evaluate the performance of these techniques, we used the first publicly available Vehicular Misbehavior Dataset VeReMi. In this paper, we will compare the performance of ML and DL in VANET for detecting and classifying misbehavior messages.