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:
Jun. 11, 2019

Filed:

Oct. 30, 2015
Applicant:

Citrix Systems, Inc., Fort Lauderdale, FL (US);

Inventors:

Nastaran Baradaran, San Jose, CA (US);

Anoop Reddy, San Jose, CA (US);

Ratnesh Singh Thakur, Sunnyvale, CA (US);

Assignee:

CITRIX SYSTEMS, INC., Fort Lauderdale, FL (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/04 (2006.01); G06N 20/00 (2019.01); H04L 12/24 (2006.01); H04L 29/06 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06N 5/045 (2013.01); H04L 41/142 (2013.01); H04L 41/16 (2013.01); H04L 63/1425 (2013.01); G06N 5/04 (2013.01);
Abstract

The present disclosure is directed towards systems and methods for improving anomaly detection using injected outliers. A normalcy calculator of a device may include a set of outliers into a training dataset of data points. The normalcy calculator, using a K-means clustering algorithm applied on the training dataset, identify at least a first cluster of data points. The normalcy calculator of the device may determine a region with a center and an outer radius that covers at least a spatial extent of the first cluster of data points. The normalcy calculator may determine a first normalcy radius for the first cluster by reducing the region around the center until a point at which all artificial outliers are excluded from a region defined by the first normalcy radius. An outlier detector of the device may use the region defined by the first normalcy radius to determine whether a new data point is normal or abnormal.


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