基于支持向量机的CAN⁃FD网络异常入侵检测

Anomaly Intrusion Detection for CANFD Bus by Support Vector Machine

针对汽车潜在的网络攻击行为,对于车载灵活数据速率控制器局域网络(CAN-FD)提出了一种基于支持向量机的异常入侵检测算法。在通用入侵检测框架(CIDF)下,该方法使用报文标识符(ID)、时间周期和数据场数据作为入侵检测特征,利用支持向量机算法的二分类特性和小样本特征,实现了对CAN-FD网络环境下入侵报文数据的识别。仿真实验数据表明,所提出的方法具有较高的入侵检测正确率,且可用于周期性报文和非周期性报文。

Aiming at the potential network attack for vehicles,an abnormal intrusion detection algorithm based on support vector machine is proposed for the controller area network with flexible data-rate(CAN-FD)bus.With the framework of common intrusion detection framework(CIDF),the method uses message identifier(ID),time period and data field as intrusion detection features.Using the binary classification and small sample features of the support vector machine algorithm,the identification of intrusion message data in CAN-FD network environment is realized.The simulation data show that the proposed method has a high accuracy of intrusion detection,and this method can be used for periodic packets and aperiodic messages.