VLOG: Vehicle Identity Verification Based on Local and Global Behavior Analysis

Abstract

Internet of Vehicles (IoV) improves traffic safety and efficiency by wireless communications among vehicles and infrastructures. To ensure secure communications in IoV, the problem of vehicle identity security must be solved before deployment. In this article, we propose a quick-response behavior-based vehicle identity verification method, called VLOG, for solving identity theft in IoV. This method is based on the idea of a vehicle usually having relatively stable traveling habit/behaivor. If we detect unusual behavior, the vehicle's identity may be stolen. VLOG captures vehicles’ latent behavior models from local and global two aspects, and further merges local and global models into a comprehensive behavior-based identity verification model. In the local part, we give a 2-D Gaussian model to fit the behavior data. In the global part, we learn vehicles’ traveling preferences under secure multiparty computation framework with considering the behavior volatility. The results of experiments based on a real-world vehicular trace dataset show the best performance of VLOG in terms of accuracy, F1 score, and cost. Meanwhile, VLOG also performs well in the area under the curve and precision-recall curve. Besides, since our model is preprepared, when a vehicle is required to be detected, the verification response time is short.