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. 24, 2020

Filed:

May. 21, 2018
Applicant:

Patternex, Inc., San Jose, CA (US);

Inventors:

Victor Chen, San Jose, CA (US);

Ignacio Arnaldo, San Jose, CA (US);

Constantinos Bassias, San Jose, CA (US);

Assignee:

Other;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); G06K 9/62 (2006.01); G06N 7/00 (2006.01); G06N 20/00 (2019.01); G06F 21/55 (2013.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); G06F 21/552 (2013.01); G06K 9/6255 (2013.01); G06K 9/6259 (2013.01); G06N 3/0454 (2013.01); G06N 3/0472 (2013.01); G06N 3/084 (2013.01); G06N 7/00 (2013.01); G06N 20/00 (2019.01); G06K 9/6247 (2013.01); G06K 9/6262 (2013.01);
Abstract

Identifying and detecting threats to an enterprise system groups log lines from enterprise data sources and/or from incoming data traffic. The process applies artificial intelligence processing to the statistical outlier in the event of the statistical outliers comprises a sparsely labelled real data set, by receiving the sparsely labelled real data set for identifying malicious data and comprising real labelled feature vectors and generating a synthetic data set comprising a plurality of synthetic feature vectors derived from the real, labelled feature vectors. The process further identifies the sparsely labelled real data set as a local data set and the synthetic data set as a global set. The process further applies a transfer learning framework for mixing the global data set with the local data set for increasing the precision recall area under curve (PR AUC) for reducing false positive indications occurring in analysis of the threats to the enterprise.


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