基于卷积神经网络的车载数字孪生持续认证方案

doi:10.11959/j.issn.1000-436x.2023229

Abstract

Abstract:

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.

Key words: autonomous vehicle, vehicular digital twin, convolutional neural network, continuous authentication, classifier

CLC Number:

TN92

Chengzhe LAI, Xinwei ZHANG, Guanjie LI, Dong ZHENG. CNN-based continuous authentication scheme for vehicular digital twin[J]. Journal on Communications, 2023, 44(11): 151-160.

Figures/Tables 9

References 25

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指标	含义
Train Loss下降	网络仍在学习
Test Loss下降
Train Loss下降	网络过拟合
Test Loss不变
Train Loss不变	数据集出现问题
Test Loss下降
Train Loss不变	学习遇到瓶颈
Test Loss不变
Train Loss上升	网络结构设计不当
Test Loss上升

CNN-based continuous authentication scheme for vehicular digital twin

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