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Title: Extracting dependencies between network assets using deep learning

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.

Inventors:
; ; ; ; ; ;
Issue Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1771624
Patent Number(s):
10833954
Application Number:
14/548,159
Assignee:
Battelle Memorial Institute (Richland, WA)
Patent Classifications (CPCs):
H - ELECTRICITY H04 - ELECTRIC COMMUNICATION TECHNIQUE H04L - TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Patent
Resource Relation:
Patent File Date: 11/19/2014
Country of Publication:
United States
Language:
English

Citation Formats

Carroll, Thomas E., Chikkagoudar, Satish, Edgar, Thomas W., Oler, Kiri J., Arthur, Kristine M., Johnson, Daniel M., and Kangas, Lars J. Extracting dependencies between network assets using deep learning. United States: N. p., 2020. Web.
Carroll, Thomas E., Chikkagoudar, Satish, Edgar, Thomas W., Oler, Kiri J., Arthur, Kristine M., Johnson, Daniel M., & Kangas, Lars J. Extracting dependencies between network assets using deep learning. United States.
Carroll, Thomas E., Chikkagoudar, Satish, Edgar, Thomas W., Oler, Kiri J., Arthur, Kristine M., Johnson, Daniel M., and Kangas, Lars J. Tue . "Extracting dependencies between network assets using deep learning". United States. https://www.osti.gov/servlets/purl/1771624.
@article{osti_1771624,
title = {Extracting dependencies between network assets using deep learning},
author = {Carroll, Thomas E. and Chikkagoudar, Satish and Edgar, Thomas W. and Oler, Kiri J. and Arthur, Kristine M. and Johnson, Daniel M. and Kangas, Lars J.},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {11}
}

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