摘要
为了解决无人驾驶通信过程中存在的车辆身份合法性问题,提出了一种基于卷积神经网络(CNN)的车载数字孪生持续认证方案进行车辆身份合法性验证。具体来说,数字孪生获取车辆传感器收集的数据,用于训练部署在数字孪生上的CNN,然后执行主成分分析为分类器选择合适的典型特征。利用CNN提取的特征,在注册阶段训练一类支持向量机(OC-SVM)分类器,在认证阶段进行数据分类,进而将当前车辆验证为合法或者恶意车辆。仿真结果表明,所提方案在性能和准确率方面优势突出并优于现有方案。
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.
作者
赖成喆
张鑫伟
李冠颉
郑东
LAI Chengzhe;ZHANG Xinwei;LI Guanjie;ZHENG Dong(School of Cyberspace Security,Xi’an University of Posts and Telecommunications,Xi’an 710121,China;School of Cyber Engineering,Xidian University,Xi’an 710126,China)
出处
《通信学报》
EI
CSCD
北大核心
2023年第11期151-160,共10页
Journal on Communications
基金
国家自然科学基金资助项目(No.61872293,No.62072371)
陕西省重点研发计划基金资助项目(No.2021ZDLGY06-02)
陕西高校青年创新团队基金资助项目。
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
autonomous vehicle
vehicular digital twin
convolutional neural network
continuous authentication
classifier
