The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 22, 2016

Filed:

May. 22, 2014
Applicant:

Accenture Global Services Limited, Dublin, IE;

Inventors:

Michael J. Lefebvre, Arlington, VA (US);

Matthew Carver, Fairfax, VA (US);

Eric Ellett, Chevy Chase, MD (US);

Walid Negm, Reston, VA (US);

Louis William DiValentin, Fairfax, VA (US);

James J. Solderitsch, Rosemont, PA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); H04L 12/26 (2006.01); H04W 12/08 (2009.01); G06F 21/56 (2013.01); H04L 12/24 (2006.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); H04L 41/12 (2013.01); H04L 43/04 (2013.01); H04L 43/0888 (2013.01); H04L 43/0894 (2013.01); H04W 12/08 (2013.01); G06F 21/566 (2013.01); H04L 41/22 (2013.01); H04L 63/1466 (2013.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining network related anomaly scores. One of the methods includes generating a network map including at least a plurality of network nodes and a plurality of edges that indicate communications paths between the plurality of network nodes, obtaining first data indicating network activity over the edges and between the plurality of network nodes for a first time period, generating a model of expected network activity over the edges and between the plurality of network nodes for a future time period using the network map and the first data, obtaining second data indicating network activity over the edges and between the plurality of network nodes for a second time period, and determining an anomaly score using a comparison between the second data and the model of expected network activity.


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