摘要
现代汽车广泛使用CAN总线结构控制车辆内的各种电子部件,但标准的CAN协议存在漏洞,易受到拒绝服务、模糊攻击和重放等攻击,而传统的基于IP协议的入侵检测技术不能直接应用于现代车辆。于是分析CAN结构,找到其缺陷;针对CAN的攻击技术,分析CAN总线特征后,融合基于频率检测、机器学习和统计检测三种异常检测方法对车辆进行入侵检测,通过实验验证,可以总体上提高现代车辆入侵检测系统的性能。
Modern vehicles widely use controller area network(CAN)bus structure to control various electronic components in vehicles,but the standard CAN protocol lacks defense mechanism and is vulnerable to be attacked such as denial of service,fuzzy attack and replay.Traditional intrusion detection technology based on IP protocol can t be directly applied in modern vehicles.The CAN structure was analyzed to find its defects.Aimed at CAN attack technology,after analyzing the CAN bus features,three anomaly detection methods based on frequency detection,machine learning and statistical detection were fused to detect vehicle intrusion detection.Through experimental verification,the performance of modern vehicle intrusion detection system is improved in general by the proposed method.
作者
赵丽
孙敏
Zhao Li;Sun Min(Department of Information Technology and Engineering,Jinzhong University,Jinzhong 030619,Shanxi,China;School of Computer and Information Technology,Shanxi University,Taiyuan 030006,Shanxi,China)
出处
《计算机应用与软件》
北大核心
2024年第2期328-332,共5页
Computer Applications and Software
基金
山西省高等学校教学改革创新项目(J2020314)
晋中学院“1331工程”创客团队项目(jzxycktd2019029)。
关键词
CAN
入侵检测
频率检测
机器学习
统计检测
CAN
Intrusion detection
Frequency detection
Machine learning
Statistical detection
