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Systems and methods for identifying infected network nodes based on anomalous behavior model

机译:基于异常行为模型识别受感染网络节点的系统和方法

摘要

The present disclosure is directed to a method of identifying an infected network node. The method includes identifying a first network node as infected. The method includes collecting a first set of network data from the first network node including anomalous activities performed by the first network node. The method includes generating an anomalous behavior model using the first set of network data. The method includes collecting a second set of network data from a second network node including anomalous activities performed by the second network node. The method includes comparing the second set of data to the generated anomalous behavior model. The method includes determining, from the comparison, that a similarity between first characteristics and second characteristics exceeds a predefined threshold. The method includes ascertaining, based on the determination, the second network node as an infected network node.
机译:本公开涉及一种识别受感染网络节点的方法。 该方法包括识别所感染的第一网络节点。 该方法包括从第一网络节点收集第一组网络数据,包括由第一网络节点执行的异常活动。 该方法包括使用第一组网络数据生成异常行为模型。 该方法包括从第二网络节点收集第二组网络数据,包括由第二网络节点执行的异常活动。 该方法包括将第二组数据与生成的异常行为模型进行比较。 该方法包括从比较确定第一特征和第二特征之间的相似性超过预定义阈值。 该方法包括基于确定第二网络节点作为受感染网络节点的确定。

著录项

  • 公开/公告号US11190433B2

    专利类型

  • 公开/公告日2021-11-30

    原文格式PDF

  • 申请/专利权人 VMWARE INC.;

    申请/专利号US201916523441

  • 发明设计人 MARCO COVA;CORRADO LEITA;

    申请日2019-07-26

  • 分类号H04L12;H04L12/26;H04L29/06;H04L12/24;

  • 国家 US

  • 入库时间 2022-08-24 22:15:12

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