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:
Apr. 19, 2022

Filed:

Dec. 21, 2016
Applicant:

Hewlett Packard Enterprise Development Lp, Houston, TX (US);

Inventors:

Pratyusa K Manadhata, Princeton, NJ (US);

Sandeep N Bhatt, Princeton, NJ (US);

Tomas Sander, Princeton, NJ (US);

Assignee:

Micro Focus LLC, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); H04L 29/08 (2006.01); H04L 29/12 (2006.01); G06N 5/02 (2006.01); G06N 20/00 (2019.01); G06F 16/2458 (2019.01); H04L 67/02 (2022.01); H04L 61/4511 (2022.01); H04L 67/306 (2022.01); H04L 67/10 (2022.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); G06F 16/2477 (2019.01); G06N 5/022 (2013.01); G06N 20/00 (2019.01); H04L 61/1511 (2013.01); H04L 67/02 (2013.01); H04L 67/10 (2013.01); H04L 67/306 (2013.01);
Abstract

A machine-readable medium may store instructions executable by a processing resource to access log data of an enterprise and extract time-series data of an enterprise entity from the log data. The time-series data may include measured feature values of a set of selected features over a series of time periods. The instructions may be further executable to train a predictive model specific to the enterprise entity using the time-series data, wherein the predictive model is to generate, for a particular time period, a predicted feature value for each of the selected features; access actual feature values of the enterprise entity for the particular time period; apply first-level deviation criteria to the actual feature value and the predicted feature value of each selected feature to identify deviant features of the enterprise entity; and apply second-level deviation criteria to the identified deviant features to identify the enterprise entity as behaving abnormally.


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