Invention Application
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
- Patent Title: EXTRACTING DEPENDENCIES BETWEEN NETWORK ASSETS USING DEEP LEARNING
- Patent Title (中): 使用深度学习提取网络资产之间的依赖关系
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Application No.: US14548159Application Date: 2014-11-19
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Publication No.: US20160142266A1Publication Date: 2016-05-19
- Inventor: Thomas E. Carroll , Satish Chikkagoudar , Thomas W. Edgar , Kiri J. Oler , Kristine M. Arthur , Daniel M. Johnson , Lars J. Kangas
- Applicant: Battelle Memorial Institute
- Applicant Address: US WA Richland
- Assignee: BATTELLE MEMORIAL INSTITUTE
- Current Assignee: BATTELLE MEMORIAL INSTITUTE
- Current Assignee Address: US WA Richland
- Main IPC: H04L12/24
- IPC: H04L12/24 ; G06N99/00

Abstract:
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.
Public/Granted literature
- US10833954B2 Extracting dependencies between network assets using deep learning Public/Granted day:2020-11-10
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