【摘要】 由于云服务器通信网中数据流量庞大且复杂，同时受到网络结构和配置的多样性以及动态变化的影响，传统的主动探测或人工分析方法难以准确识别漏洞弧段，导致故障数据识别的准确性和效率受到限制。因此，研究一种基于被动分簇的云服务器通信串口故障数据识别方法。由被动分簇算法确定云服务器通信串口的通信网的漏洞弧段，基于信息熵的量化方法，提取云服务器通信串口通信网漏洞弧段中节点流量数据的熵值特征，将其作为串口故障数据分类方法的分类目标，并以K-means聚类的方式判定云服务器通信串口流量数据的故障类型，实现被动分簇下云服务器通信串口故障数据识别。实验结果表明，所提方法在多种网络入侵行为下对云服务器通信串口故障数据识别时，都有较好的识别效果。更多还原

【Abstract】 Due to the large and complex data flow in the cloud server communication network, as well as the diversity and dynamic changes in network structure and configuration, traditional active detection or manual analysis methods are difficult to accurately identify vulnerability arcs, which limits the accuracy and efficiency of fault data identification. Therefore, a method of cloud server communication serial port fault data identification based on passive clustering is studied. The passive clustering algorithm is used to determine the vulnerability arc of the communication network of the cloud server communication serial port.Based on the quantification method of information entropy, the entropy characteristics of node traffic data in the vulnerability arc of the cloud server communication serial port communication network are extracted, which are used as the classification target of the serial port fault data classification method. K-means clustering is used to determine the type of fault in the cloud server communication serial port traffic data and realize the identification of cloud server communication serial port fault data under passive clustering. The experimental results show that the proposed method has good recognition performance for cloud server communication serial port fault data under various network intrusion behaviors.更多还原