发明申请
US20160142266A1 EXTRACTING DEPENDENCIES BETWEEN NETWORK ASSETS USING DEEP LEARNING
审中-公开
使用深度学习提取网络资产之间的依赖关系
- 专利标题: EXTRACTING DEPENDENCIES BETWEEN NETWORK ASSETS USING DEEP LEARNING
- 专利标题(中): 使用深度学习提取网络资产之间的依赖关系
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申请号: US14548159申请日: 2014-11-19
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公开(公告)号: US20160142266A1公开(公告)日: 2016-05-19
- 发明人: Thomas E. Carroll , Satish Chikkagoudar , Thomas W. Edgar , Kiri J. Oler , Kristine M. Arthur , Daniel M. Johnson , Lars J. Kangas
- 申请人: Battelle Memorial Institute
- 申请人地址: US WA Richland
- 专利权人: BATTELLE MEMORIAL INSTITUTE
- 当前专利权人: BATTELLE MEMORIAL INSTITUTE
- 当前专利权人地址: US WA Richland
- 主分类号: H04L12/24
- IPC分类号: H04L12/24 ; G06N99/00
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.
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