An Access Control Mechanism Based on Risk Prediction for the IoV

Abstract

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.