Integrating Plausibility Checks and Machine Learning for Misbehavior Detection in VANET

Abstract

The safety and efficiency of vehicular communications rely on the correctness of the data exchanged between vehicles. In this paper we address the issue of detecting and classifying location spoofing misbehavior using the VeReMi dataset. We propose a framework for a system that uses plausibility checks as a feature vector for machine learning models, used to detect and classify misbehavior. Using KNN and SVM, our results show we can improve the overall detection precision of the plausibility checks used in the feature vectors by over 20%, while maintaining a recall within 5%. We have also proven once a misbehavior has been detected it is possible to classify different types of known misbehavior's. Classifying the misbehavior types allows for more accurate and specific action steps to counteract the attacks, hence improving the ability to recover safety and security in the system.