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汽车安全与节能学报

汽车安全与节能学报 ›› 2024, Vol. 15 ›› Issue (5): 660-669.DOI: 10.3969/j.issn.1674-8484.2024.05.004

• 智能驾驶与智慧交通 • 上一篇    下一篇

基于改进GAN的智能网联车CAN总线异常检测研究

杨浩然1,2(), 谢辉1,2,*(), 宋康1,2, 闫龙1,2   

  1. 1.天津大学 未来技术学院，天津 300072，中国
    2.天津大学 发动机国家重点实验室，天津 300072，中国
  • 收稿日期:2024-02-23 修回日期:2024-08-27 出版日期:2024-10-31 发布日期:2024-11-07
  • 通讯作者: 谢辉，教授。E-mail：jianjiecq@163.com。
  • 作者简介:杨浩然（1998—），男（土家族），湖北，硕士研究生。E-mail：838693115@qq.com。
  • 基金资助:
    天津市科技计划项目(19ZXZNGX0005)

Research on CAN bus anomaly detection of intelligent networked vehicle based on improved GAN

YANG Haoran1,2(), XIE Hui1,2,*(), SONG Kang1,2, YAN Long1,2   

  1. 1. School of Future Technology, Tianjin University, Tianjin 300072, China
    2. State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China
  • Received:2024-02-23 Revised:2024-08-27 Online:2024-10-31 Published:2024-11-07

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摘要：

为提高智能网联汽车（ICVs）的安全性，提出了一种能适应异常流量低的、泛化能力强的控制器局域网（CAN）总线异常检测算法，以应对车辆可能会产生的潜在的和难以察觉的异常情况，有效提高异常数据的检测精度。该文探讨了生成对抗网络（GANs）的理论意义，并在一辆智能网联公交车上，收集了4类不同攻击数据，2类罕见报警数据，基于计算数据的重构误差来衡量异常程度，验证算法的适应性。 结果表明：该文提出的算法在低流量数据集Data4上的F1分数和误报率分别达到98.31%和2.90%，超过初始模型及深度卷积生成对抗网络（DCGAN）算法，且对罕见报警数据的误报率减少到3%，说明该算法适用于低流量异常检测，且泛化能力强。

关键词: 智能网联汽车（ICVs）, 控制器局域网（CAN）总线, 异常检测, 低流量异常, 隐空间距离

Abstract:

A novel Controller Area Network (CAN) bus anomaly detection algorithm characterized by its adaptability to low anomaly traffic and strong generalization capability was proposed to enhance the safety of Intelligent Connected Vehicles (ICVs). The algorithm aimed to address potential and hard-to-detect abnormalities that may arise in vehicles, significantly improving the detection accuracy of anomalous data. This study explored the theoretical significance of Generative Adversarial Networks (GANs) and collected four types of attack data and two types of rare alarm data from an intelligent connected bus. The anomaly degree was assessed based on the reconstruction error of the computed data to validate the algorithm's adaptability. The results show that the proposed algorithm achieves an F1 score of 98.31% and a false positive rate of 2.90% on the low-traffic dataset Data4, surpassing the baseline model and the Deep Convolutional GAN (DCGAN) algorithm. Moreover, the false positive rate for rare alarm data is reduced to 3%, indicating that the algorithm is well-suited for low-traffic anomaly detection and exhibits strong generalization capabilities.

Key words: intelligent connected vehicles (ICVs), controller area network (CAN)-bus, anomaly detection, low-traffic anomaly, latent space distance

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