Anomalous identity recognition model based on vehicle driving characteristic verification in typical scenarios

Vehicle-to-everything (V2X) enables the exchange and sharing of information between vehicles and the outside world, which improves driving safety, reduces traffic congestion, and enhances traffic efficiency. However, this information exchange and transmission of massive data also exposes many attack surfaces, which may result in security incidents such as vehicle theft, information leakage, and driving failure. Traditional methods to ensure traffic information interaction through information security have limitations. This paper proposes an innovative model for anomalous identity recognition based on vehicle driving characteristic verification. The model aims to ensure consistency among the speed data from different sources, types of transmission data, and perception data obtained by sensors. The model is based on a multi-class support vector machine (multi-class SVM) to identify vehicle behavior and a bidirectional gated recurrent unit (BiGRU) neural network to predict vehicle speed. A credible calculation method was designed to calculate the error between the predicted speed and the actual collected speed in the car-following and lane-changing scenarios. The Next Generation Simulation dataset was used to train and test the models. The experimental results showed that the overall recognition accuracy of the multi-class SVM model was 95.50 %, the predicted precision with an order of magnitude of cm/s was achieved by the BiGRU model, and the overall recognition accuracy of the model was >90 %. The public key infrastructure (PKI) scheme is currently the mainstream scheme of information security in the Internet of Vehicles. This paper analyzes the feasibility of the proposed anomalous identity recognition model applied in the PKI framework, which can effectively identify anomalous vehicle identities by discriminating the vehicle speed and effectively ensure the security between a vehicle and the external network communication (4G/5G/V2X).