Misbehavior Detection using Machine Learning in Vehicular Communication Networks

TLDR: A machine learning based misbehavior detection system which is trained using datasets generated through extensive simulation based on realistic vehicular network environment and outperforms previous methods in terms of accurately identifying various misbehavior.

Abstract: Vehicular networks are susceptible to variety of attacks such as denial of service (DoS) attack, sybil attack and false alert generation attack. Different cryptographic methods have been proposed to protect vehicular networks from these kind of attacks. However, cryptographic methods have been found to be less effective to protect from insider attacks which are generated within the vehicular network system. Misbehavior detection system is found to be more effective to detect and prevent insider attacks. In this paper, we propose a machine learning based misbehavior detection system which is trained using datasets generated through extensive simulation based on realistic vehicular network environment. The simulation results demonstrate that our proposed scheme outperforms previous methods in terms of accurately identifying various misbehavior.