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. 21, 2026

Filed:

Dec. 10, 2021
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Gary I. Givental, Bloomfield Hills, MI (US);

Joel Rajakumar, Atlanta, GA (US);

Aankur Bhatia, Bethpage, NY (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 21/57 (2013.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 5/045 (2023.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G06F 21/577 (2013.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 5/045 (2013.01); G06N 20/20 (2019.01);
Abstract

An apparatus, a method, and a computer program product are provided that dynamically selects features and machine learning models for optimal accuracy when determining a threat disposition of a security alert. The method includes training a base machine learning model, determining impacts that features in the training dataset have on the trained base machine learning model when predicting threat disposition on security threats, and creating subsets of the features, based on threat dispositions, by analyzing the features with their corresponding impacts and placing common features and impacts into each subset of the subsets. The method also includes training a plurality of machine learning models and a machine learning feature predictor using the training dataset and the subsets. The method further includes selecting, for a new input data instance, the selected features from the new input data instance and selecting a trained machine learning model trained based on the selected features.


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