通信学报 ›› 2023, Vol. 44 ›› Issue (11): 151-160.doi: 10.11959/j.issn.1000-436x.2023229

• 学术论文 • 上一篇    

基于卷积神经网络的车载数字孪生持续认证方案

赖成喆1, 张鑫伟1, 李冠颉2, 郑东1   

  1. 1 西安邮电大学网络空间安全学院，陕西 西安 710121
    2 西安电子科技大学网络与信息安全学院，陕西 西安 710126
  • 修回日期:2023-09-13 出版日期:2023-11-01 发布日期:2023-11-01
  • 作者简介:赖成喆（1985− ），男，陕西汉中人，博士，西安邮电大学教授，主要研究方向为安全协议设计与分析、车联网安全
    张鑫伟（1998− ），男，陕西咸阳人，西安邮电大学硕士生，主要研究方向为车联网安全和隐私保护技术
    李冠颉（1994− ），男，陕西韩城人，西安电子科技大学博士生，主要研究方向为数字孪生和车联网
    郑东（1964− ），男，山西临汾人，博士，西安邮电大学教授，主要研究方向为编码密码学和网络安全
  • 基金资助:
    国家自然科学基金资助项目(61872293);国家自然科学基金资助项目(62072371);陕西省重点研发计划基金资助项目(2021ZDLGY06-02);陕西高校青年创新团队基金资助项目

CNN-based continuous authentication scheme for vehicular digital twin

Chengzhe LAI1, Xinwei ZHANG1, Guanjie LI2, Dong ZHENG1   

  1. 1 School of Cyberspace Security, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
    2 School of Cyber Engineering, Xidian University, Xi’an 710126, China
  • Revised:2023-09-13 Online:2023-11-01 Published:2023-11-01
  • Supported by:
    The National Natural Science Foundation of China(61872293);The National Natural Science Foundation of China(62072371);The Key Research and Development Program of Shaanxi Province(2021ZDLGY06-02);The Youth Innovation Team of Shaanxi Universities Foundation

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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。

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

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

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