Machine Learning for Misbehavior Detection in Next-Generation Vehicular Networks

Abstract

Connected vehicles have great potential to enhance road safety, reduce traffic congestion, and play a vital role in green engineering by reducing pollution and fuel consumption. By enabling more efficient traffic flow, eco-routing, and optimized driving behaviors, connected vehicles contribute to a cleaner environment. However, when a vehicle is compromised, it can pose a serious threat to the entire network due to the potential harm it can cause. One of the major challenges in vehicular networks is the detection of misbehaving vehicles, which should then be blacklisted or their certificates revoked. In this work, we propose a novel scheme that leverages machine learning to accurately detect and classify vehicle behavior, enabling effective identification and management of misbehaving vehicles. To assess the effectiveness of our approach, a comprehensive comparative analysis was performed. The results demonstrate that our model outperforms existing methods in accurately classifying vehicle behaviors, highlighting its potential for real-world deployment in securing vehicular networks.