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:
Jul. 22, 2025

Filed:

Oct. 13, 2021
Applicant:

Oracle International Corporation, Redwood Shores, CA (US);

Inventors:

Ján Šterba, Bratislava, SK;

Venkatakrishnan Gopalakrishnan, Ontario, CA;

May Bich Nhi Lam, San Jose, CA (US);

Yunjiao Xue, Ontario, CA;

Nana Lei, San Francisco, CA (US);

Edward C. Cheng, South San Francisco, CA (US);

Hayward Ivan Craig Welcher, Waterloo, CA;

Jacob Becker West, San Francisco, CA (US);

Qi Wen Cao, Ontario, CA;

Assignee:

Oracle International Corporation, Redwood Shores, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); G06N 20/00 (2019.01); H04L 9/40 (2022.01);
U.S. Cl.
CPC ...
H04L 63/1425 (2013.01); G06N 20/00 (2019.01); H04L 63/1416 (2013.01); H04L 63/1433 (2013.01); H04L 63/1441 (2013.01); H04L 63/1466 (2013.01); H04L 2463/082 (2013.01);
Abstract

Machine-learning (ML) techniques and models are described for predicting the number and severity of network attacks within a specified timeframe, such as the next fifteen minutes. In some embodiments, the techniques including training a ML model based on features extracted from a training dataset and applying the trained ML model to estimate (a) the probability of an attack happening on an account within a specified timeframe; (b) how many attacks are predicted to occur in the specified timeframe (if any); and/or (c) the severity of the attacks predicted to occur. A system may deploy preventative measures based on the ML model output to counter or mitigate the effects of predicted and coordinated network attacks.


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