Patent ID: 11921851
Assignee: MUSARUBRA US LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A method, comprising:
generating a plurality of trained machine learning models, each trained machine learning model of the plurality of trained machine learning models to classify whether a digital resource is associated with a respective cyberattack of a plurality of cyberattacks;
deploying, to one or more compute devices via a network, a first machine learning model and a second machine learning model from the plurality of trained machine learning models and a third machine learning model from the plurality of trained machine learning models, the first machine learning model and the second machine learning model to be executed by the one or more compute devices in an in-line mode to classify whether a plurality of digital resources is associated with any cyberattack from the plurality of cyberattacks and determine remedial actions, the third machine learning model to be executed by the one or more compute devices in an out-of-band mode to classify whether the plurality of digital resources is associated with any cyberattack from the plurality of cyberattacks and not determine any remedial actions even when the third machine learning model classifies the digital resource from the plurality of digital resources being associated with a cyberattack from the plurality of cyberattacks;
sending a signal to the one or more compute devices to change, in response to an evaluation of a performance of the first machine learning model, the second machine learning model, and a performance of the third machine learning model, a configuration of the first machine learning model and the second machine learning model, and a configuration of the third machine learning model between the in-line mode and the out-of-band mode; and
deploying, to the one or more compute devices via the network, a fourth machine learning model from the plurality of trained machine learning models different from the first machine learning model, the second machine learning model, and the third machine learning model to operate in the in-line mode when the first machine learning model, the second machine learning model, and the third machine learning model fail to satisfy a performance threshold.