汽车工程 ›› 2023, Vol. 45 ›› Issue (6): 922-935.doi: 10.19562/j.chinasae.qcgc.2023.ep.002
所属专题: 智能网联汽车技术专题-控制2023年
收稿日期:
2022-09-04
修回日期:
2022-09-21
出版日期:
2023-06-25
发布日期:
2023-03-17
通讯作者:
崔哲
E-mail:cuizhe@ncst.edu.cn
基金资助:
Yuxin Guan1,Haojie Ji2,Zhe Cui1(),He Li1,Liwen Chen1
Received:
2022-09-04
Revised:
2022-09-21
Online:
2023-06-25
Published:
2023-03-17
Contact:
Zhe Cui
E-mail:cuizhe@ncst.edu.cn
摘要:
智能汽车与车联网技术不断融合,汽车正朝着智能化和网联化方向发展。随着车载网络(例如:CAN网络)的复杂性以及车辆与外界相连的方式增加,汽车面临的网络安全风险大幅上升。入侵检测系统作为保护车载网络安全的重要屏障,可以有效检测外部入侵和车辆异常行为。首先,介绍了车载网络的安全属性,并分析了智能网联汽车的网络安全问题以及车载CAN网络的脆弱性和对其的攻击方式。其次,总结了近几年车载CAN网络入侵检测方法的研究现状。最后,对未来车载网络入侵检测系统的发展提出几项开放性问题。
关宇昕,冀浩杰,崔哲,李贺,陈丽文. 智能网联汽车车载CAN网络入侵检测方法综述[J]. 汽车工程, 2023, 45(6): 922-935.
Yuxin Guan,Haojie Ji,Zhe Cui,He Li,Liwen Chen. An Overview of Intrusion Detection Methods for In-Vehicle CAN Network of Intelligent Networked Vehicles[J]. Automotive Engineering, 2023, 45(6): 922-935.
表3
车载CAN网络常见攻击方式对比"
攻击方式 | 威胁程度 | 防护难度 | 攻击结果 |
---|---|---|---|
嗅探攻击 | 中 | 高 | 用户隐私信息和车辆信息泄露 |
DoS攻击 | 高 | 低 | 大量高优先级报文占用CAN网络,造成通信故障甚至车载的网络崩溃 |
重放攻击 | 高 | 中 | 未作修改的报文重放CAN网络中,使网络负载率和ID熵增加,影响通信功能 |
篡改攻击 | 高 | 中 | 将CAN网络中报文的内容修改,产生错误帧和信号,影响通信功能 |
消息注入攻击 | 高 | 中 | 直接向CAN网络中注入恶意报文,网络负载率和ID熵增加,使汽车内部功能紊乱甚至导致汽车瘫痪 |
欺骗攻击 | 高 | 中 | 将欺骗性报文注入到CAN网络,使网络负载率和ID熵增加,导致汽车突然失控 |
丢弃攻击 | 高 | 高 | 直接删除CAN网络中的报文,ID熵降低,可能使汽车某些重要功能失效 |
模糊攻击 | 高 | 高 | 将变异算法转换的随机数据注入到CAN网络中,使网络负载率和ID熵增加 |
伪装攻击 | 高 | 中 | 模拟节点向车载网络发送伪造的报文,使网络负载率和ID熵增加 |
表5
基于信息论与统计分析的车载CAN网络入侵检测方法"
文献 | 时间 | 检测技术 | 检测的攻击方式 | 存在的问题 | 数据集来源 | 数据集是否开源 |
---|---|---|---|---|---|---|
[ | 2011 | 信息熵、相对熵 | DoS、伪装和消息注入攻击 | 检测小规模攻击模式的能力有限 | 实车数据 | 否 |
[ | 2016 | 信息熵 、CAN报文相对距离 | DoS和重放攻击 | 检测技术的灵敏性不足 | 软件模拟数据 | 否 |
[ | 2016 | 信息熵、熵值偏离程度 | 消息注入和模糊攻击 | 检测小量伪装攻击消息的能力有限 | 实车数据 | 否 |
[ | 2018 | 信息熵、模拟退火算法优化滑动窗口 | DoS和消息注入攻击 | 需考虑车辆运行状态对信息熵的影响 | 实车数据 | [ |
[ | 2022 | 图形论、卡方检验、统计分析 | DoS、欺骗、模糊和重放攻击 | 检测所用图形属性较单一 | 实车数据 | 否 |
[ | 2020 | 信息熵、滑动窗口相似性 | DoS攻击 | 检测系统的功能较单一 | 实车数据 | [ |
[ | 2012 | 检测异常CAN ID和频率 | DoS和消息注入攻击 | 检测小规模攻击模式的能力有限 | 软件模拟数据 | 否 |
[ | 2017 | 远程帧请求与响应之间的偏移率和时间间隔 | DoS和模糊攻击 | 未给出该系统的检测精度和其他性能指标 | 实车数据 | [ |
[ | 2017 | ID相同且连续的CAN消息有效载荷之间的汉明距离 | 欺骗和消息注入攻击 | 对重放攻击的检测能力有限 | 实车数据 | 否 |
[ | 2018 | 时间序列算法、检查CAN ID广播间隔的变化 | 重放和消息注入攻击 | 需优化阈值、模型因子和参数来提高性能 | 实车数据 | 否 |
表6
基于机器学习的车载CAN网络入侵检测方法"
文献 | 年份 | 检测技术 | 检测的攻击方式 | 存在的问题 | 数据集来源 | 数据集是否开源 |
---|---|---|---|---|---|---|
[ | 2016 | HMM | 消息注入攻击 | 需进一步研究如何处理罕见异常状态以及如何确定最佳阈值 | 实车数据 | 否 |
[ | 2018 | HMM、回归模型 | 添加噪声攻击 | 该研究没有提供检测模型的训练时间和检测延迟 | 软件模拟数据 | 否 |
[ | 2019 | 改进的BAT算法、OCSVM | DoS攻击 | 该研究没有提供检测模型的训练时间 | 实车数据 | 否 |
[ | 2020 | RT、RF、SGD和NB | DoS、模糊、欺骗驱动装置和欺骗RPM仪表攻击 | 该研究没有提供检测模型的训练时间 | 实车数据 | [ [ |
[ | 2021 | KNN、RF、SVM和MLP | DoS、模糊、欺骗驱动装置和欺骗RPM仪表攻击 | 该研究没有提供检测模型的训练时间 | 实车数据 | [ [ |
[ | 2018 | GAN | DoS、模糊、欺骗驱动装置和欺骗RPM仪表攻击 | 无法准确区分流量异常是电子元件故障引起的还是黑客恶意攻击引起的 | 实车数据 | [ |
[ | 2021 | GAN | DoS、消息注入、伪装和篡改 攻击 | 需优化GAN模型以减少运算资源,将其轻量化 | 实车数据 | 否 |
[ | 2016 | LSTM | DoS攻击 | 该方法将每个ID的数据序列视为相互独立的 | 实车数据 | 否 |
[ | 2021 | LSTM | 重放和篡改攻击 | 计算速度较慢,未来需要将其轻量化 | 实车数据 | [ |
[ | 2022 | LSTM | DoS、模糊、欺骗驱动装置和欺骗RPM仪表攻击 | 该研究没有提供检测模型的训练时间和检测延迟 | 实车数据 | [ |
[ | 2020 | DCNN | DoS、模糊、欺骗驱动装置和欺骗RPM仪表攻击 | 需进一步研究如何将其在线应用在汽车中 | 实车数据 | [ |
[ | 2022 | CNN | DoS、模糊、欺骗驱动装置和欺骗RPM仪表攻击 | 只能检测到干扰CAN数据包正常序列的攻击 | 实车数据 | [ |
[ | 2022 | CNN、LSTM | DoS、模糊、欺骗驱动装置和欺骗RPM仪表攻击 | 该研究没有提供检测模型的训练时间和检测延迟 | 实车数据 | [ |
[ | 2021 | CNN、AGRU | DoS、模糊和伪装攻击 | 该研究没有提供检测模型的训练时间和检测延迟 | 实车数据 | [ |
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