CNN-based continuous authentication scheme for vehicular digital twin - Chengzhe LAI; Xinwei ZHANG; Guanjie LI; Dong ZHENG (Tongxin xuebao)
To address vehicle identity legitimacy verification issues, a continuous authentication scheme for vehicular digital twin based on convolutional neural network (CNN) was proposed.Specifically, the digital twin was used to acquire the data collected by the vehicle sensors for training the CNN deployed on the digital twin.Then, principal component analysis was performed to select appropriate typical features for the classifier.Using the features extracted by the CNN, the one-class support vector machine (OC-SVM) classifier was trained in the registration phase and the data was classified in the authentication phase, which consequently verified the current vehicle as a legitimate or malicious vehicle.Simulation results show that the proposed scheme has outstanding advantages and outperforms the existing schemes in terms of performance and accuracy.