Effective Misbehaviour Detection System Based on Machine Learning Approaches for V2V

Abstract

VANET technology has been evolving and now includes vehicle communication to other vehicles as well as to infrastructure. Vehicle-to-Everything (V2X) communication refers to the expansion of the vehicle network to include communication between vehicles and all intelligent roadside devices. Due to the nature of the vehicular network that includes heterogeneous nodes, varying speeds, and sporadic connections, the vehicle network poses numerous issues for which conventional security measures are not always successful. As a result, extensive research has been conducted to develop security solutions while taking network requirements and performance into consideration. In this paper, we provide an extensive taxonomy and overview of current V2V communication technology security solutions. We propose an effective misbehavior detection of sybil attacks in V2V communication based on a machine learning model. We demonstrate the effectiveness of this model, and we highlight future research challenges.