With the development of intelligent and netw orking technology in automobile,the malicious attacks against in-vehicle CAN netw orks are increasing day by day,and the problem of information safety in automobile is aggr...With the development of intelligent and netw orking technology in automobile,the malicious attacks against in-vehicle CAN netw orks are increasing day by day,and the problem of information safety in automobile is aggravated. In this regard,this paper analyzes the security loopholes and threats w hich the CAN bus faced,put forw ard a kind of anomaly detection algorithm for vehicle CAN bus. The method uses support vector machine algorithm to distinguish betw een normal message and abnormal message,so as to realize the CAN bus anomaly detection. Theoretical and experimental studies show that this method can effectively detect abnormal packets in the CAN bus w ith a detection rate of over 90%,w hich can effectively resist malicious attacks such as tampering and cheating on the vehicle CAN bus.展开更多
为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU...为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。展开更多
文摘With the development of intelligent and netw orking technology in automobile,the malicious attacks against in-vehicle CAN netw orks are increasing day by day,and the problem of information safety in automobile is aggravated. In this regard,this paper analyzes the security loopholes and threats w hich the CAN bus faced,put forw ard a kind of anomaly detection algorithm for vehicle CAN bus. The method uses support vector machine algorithm to distinguish betw een normal message and abnormal message,so as to realize the CAN bus anomaly detection. Theoretical and experimental studies show that this method can effectively detect abnormal packets in the CAN bus w ith a detection rate of over 90%,w hich can effectively resist malicious attacks such as tampering and cheating on the vehicle CAN bus.
文摘为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。
