通信学报2023，Vol.44Issue(11)：151-160,10.DOI:10.11959/j.issn.1000-436x.2023229
基于卷积神经网络的车载数字孪生持续认证方案
CNN-based continuous authentication scheme for vehicular digital twin
摘要
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 ac-quire the data collected by the vehicle sensors for training the CNN deployed on the digital twin.Then,principal compo-nent 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.关键词
无人驾驶/车载数字孪生/卷积神经网络/持续认证/分类器Key words
autonomous vehicle/vehicular digital twin/convolutional neural network/continuous authentication/classifier分类
信息技术与安全科学引用本文复制引用
赖成喆,张鑫伟,李冠颉,郑东..基于卷积神经网络的车载数字孪生持续认证方案[J].通信学报,2023,44(11):151-160,10.基金项目
国家自然科学基金资助项目(No.61872293,No.62072371) （No.61872293,No.62072371）
陕西省重点研发计划基金资助项目(No.2021ZDLGY06-02) （No.2021ZDLGY06-02）
陕西高校青年创新团队基金资助项目 The National Natural Science Foundation of China(No.61872293,No.62072371),The Key Research and De-velopment Program of Shaanxi Province(No.2021ZDLGY06-02),The Youth Innovation Team of Shaanxi Universities Foundation （No.61872293,No.62072371）
