信息网络安全

信息网络安全 ›› 2025, Vol. 25 ›› Issue (6): 872-888.doi: 10.3969/j.issn.1671-1122.2025.06.003

• 专题论文: 网络主动防御 • 上一篇    下一篇

基于联邦学习的智能汽车CAN总线入侵检测系统

荀毅杰1,2, 崔嘉容1,2, 毛伯敏1,2(), 秦俊蔓1,2   

  1. 1.西北工业大学网络空间安全学院，西安 710072
    2.西北工业大学深圳研究院，深圳 518057
  • 收稿日期:2025-02-25 出版日期:2025-06-10 发布日期:2025-07-11
  • 通讯作者: 毛伯敏 maobomin@nwpu.edu.cn
  • 作者简介:荀毅杰（1994—），男，山西，副教授，博士，主要研究方向为智能车联网安全、人工智能和信息安全|崔嘉容（2002—），女，河北，硕士研究生，主要研究方向为智能车联网安全|毛伯敏（1989—），男，湖北，教授，博士，主要研究方向为空天地一体化网络、卫星物联网、车联网、边缘计算|秦俊蔓（1996—），女，安徽，博士研究生，主要研究方向为智能车联网安全、人工智能和信息安全。
  • 基金资助:
    国家自然科学基金(62202386);国家自然科学基金(62402389);广东省基础与应用基础研究基金(2024A1515011198);广东省基础与应用基础研究基金(2024A1515010209);广东省基础与应用基础研究基金(2023A1515110079)

Intrusion Detection System for the Controller Area Network Bus of Intelligent Vehicles Based on Federated Learning

XUN Yijie1,2, CUI Jiarong1,2, MAO Bomin1,2(), QIN Junman1,2   

  1. 1. School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
    2. Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
  • Received:2025-02-25 Online:2025-06-10 Published:2025-07-11

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摘要：

智能汽车已经成为人们日常出行必不可少的交通工具。控制器局域网总线（CAN）作为智能汽车内部的核心通信协议总线，其安全问题备受关注。CAN总线因通信接口访问控制薄弱、数据交互缺乏认证、报文无源/目的地址等因素使车辆易受到恶意攻击。车内网关、防火墙等安全方案受车内带宽和计算资源的限制，难以搭载强大的加密认证算法，防护能力受限。而现有的基于单类旁路特征（电压、时钟或数据流等）的入侵检测系统（IDS），检测攻击能力受限，如基于时钟偏斜的IDS无法检测非周期性攻击。为此，文章提出一种基于联邦学习的智能汽车CAN总线入侵检测系统。车端收集多维度特征数据进行轻量化训练后将参数传到云端，云端通过异步横向联邦学习结构收集不同车辆传过来的参数，用极端梯度提升算法开展深度训练，并将训练好的模型参数传给车端，车端进行检测并溯源。在3款不同品牌真实车辆上的实验结果表明，该系统能够高精度检测6种典型的攻击类型，包括总线关闭、欺骗、同源、模糊、伪装和重放攻击，平均检测时延为0.0987ms。

关键词: 联邦学习, 智能汽车, 入侵检测, CAN总线

Abstract:

Intelligent vehicles have become an essential transportation tool for human daily travel. The Controller Area Network (CAN), a core communication protocol inside intelligent vehicles, faces significant security concerns. The CAN bus is vulnerable to malicious attacks due to factors such as weak communication interface access control, lack of authentication in data exchange, and the absence of source/destination addresses in messages. In-vehicle gateways and firewalls are limited by bandwidth and computational resources. It makes difficult to implement powerful encryption and authentication algorithms, which restricts their protective capabilities. Current Intrusion Detection Systems (IDS) that rely on single-class side-channel features, like voltage, clock, or data flow, have limited ability to detect various types of attacks. For example, the IDS based on clock skew cannot detect attacks that are not periodic. This study proposed a federated learning-based CAN bus intrusion detection system for intelligent vehicles. The vehicle collected multidimensional feature data for lightweight training and transmitted parameters to the cloud. The cloud gathered parameters from different vehicles using an asynchronous horizontal federated learning structure, conducted deep training with the eXtreme Gradient Boosting (XGBoost) algorithm, and sent trained model parameters back to the vehicle. The vehicle then performed detection and attack source tracing. Experiments on three real vehicles from different brands demonstrated that the system achieves high-precision detection of six typical attack types, including Bus-off, Spoofing, Same Origin MethodExecution (SOME), Fuzzing, Masquerade, and Replay attacks. The average detection latency was 0.0987 ms.

Key words: federated learning, intelligent vehicle, intrusion detection system, CAN bus

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