-
1.发明申请公开(公告)号：US20160142266A1
公开(公告)日：2016-05-19
申请号：US14548159
申请日：2014-11-19
发明人： Thomas E. Carroll , Satish Chikkagoudar , Thomas W. Edgar , Kiri J. Oler , Kristine M. Arthur , Daniel M. Johnson , Lars J. Kangas
CPC分类号： H04L41/16 , H04L41/14 , H04L43/026
摘要： A network analysis tool receives network flow information and uses deep learning—machine learning that models high-level abstractions in the network flow information—to identify dependencies between network assets. Based on the identified dependencies, the network analysis tool can discover functional relationships between network assets. For example, a network analysis tool receives network flow information, identifies dependencies between multiple network assets based on evaluation of the network flow information, and outputs results of the identification of the dependencies. When evaluating the network flow information, the network analysis tool can pre-process the network flow information to produce input vectors, use deep learning to extract patterns in the input vectors, and then determine dependencies based on the extracted patterns. The network analysis tool can repeat this process so as to update an assessment of the dependencies between network assets on a near real-time basis.
摘要翻译： 网络分析工具接收网络流量信息，并使用深入学习机器学习，对网络流量信息中的高级抽象进行建模，以识别网络资产之间的依赖关系。 基于确定的依赖关系，网络分析工具可以发现网络资产之间的功能关系。 例如，网络分析工具接收网络流信息，基于网络流信息的评估来识别多个网络资产之间的依赖关系，并输出依赖关系的识别结果。 在评估网络流量信息时，网络分析工具可以预处理网络流信息以产生输入向量，使用深度学习提取输入向量中的模式，然后基于提取的模式确定依赖关系。 网络分析工具可以重复此过程，以便更新几乎实时的网络资产之间的依赖关系的评估。
-
公开(公告)号：US10833954B2
公开(公告)日：2020-11-10
申请号：US14548159
申请日：2014-11-19
发明人： Thomas E. Carroll , Satish Chikkagoudar , Thomas W. Edgar , Kiri J. Oler , Kristine M. Arthur , Daniel M. Johnson , Lars J. Kangas
摘要： A network analysis tool receives network flow information and uses deep learning—machine learning that models high-level abstractions in the network flow information—to identify dependencies between network assets. Based on the identified dependencies, the network analysis tool can discover functional relationships between network assets. For example, a network analysis tool receives network flow information, identifies dependencies between multiple network assets based on evaluation of the network flow information, and outputs results of the identification of the dependencies. When evaluating the network flow information, the network analysis tool can pre-process the network flow information to produce input vectors, use deep learning to extract patterns in the input vectors, and then determine dependencies based on the extracted patterns. The network analysis tool can repeat this process so as to update an assessment of the dependencies between network assets on a near real-time basis.
-
搜索结果
2 条记录 (耗时 0.01 秒)
