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EXTRACTING DEPENDENCIES BETWEEN NETWORK ASSETS USING DEEP LEARNING

United States Patent Application

20160142266
A1
View the Complete Application at the US Patent & Trademark Office
Pacific Northwest National Laboratory - Visit the Technology Commercialization Program Website
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.
Carroll, Thomas E. (Richland, WA), Chikkagoudar, Satish (Richland, WA), Edgar, Thomas W. (Richland, WA), Oler, Kiri J. (Tacoma, WA), Arthur, Kristine M. (West Lafayette, IN), Johnson, Daniel M. (Kennewick, WA), Kangas, Lars J. (West Richland, WA)
BATTELLE MEMORIAL INSTITUTE (Richland WA)
14/ 548,159
November 19, 2014
STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY-SPONSORED RESEARCH AND DEVELOPMENT [0001] This invention was made with government support under Contract DE-AC0576RLO1830 awarded by the U.S. Department of Energy. The government has certain rights in the invention.