Back to results
US 11,770,404 B2Granted
Enhanced neutral domain data selection for cybersecurity machine learning applications
AssigneeDomain Tools, LLC
InventorsSean M. McNee, John W. Conwell
Life of this patent
Estimating the term…
Loading patent…
Loading patent…
AssigneeDomain Tools, LLC
InventorsSean M. McNee, John W. Conwell
Estimating the term…
Methods, systems, and techniques for producing and using enhanced machine learning models and computer-implemented tools to investigate cybersecurity related data and threat intelligence data are provided. Example embodiments provide an Enhanced Predictive Security System, for building, deploying, and managing applications for evaluating threat intelligence data that can predict malicious domains associated with bad actors before the domains are known to be malicious. In one example, the EPSS comprises one or more components that work together to provide an architecture and a framework for building and deploying cybersecurity threat analysis application, including machine learning algorithms, feature class engines, tuning systems, ensemble classifier engines, and validation and testing engines. These components cooperate and act upon domain data and feature class vectors to create sampled test, training, and validation data and to build model subsets and applications using a trained model library, which stores definitions of each model subset for easy re-instantiation.
A method in a computing system for determining neutral domain data for use with machine learning software applications, comprising:
A computing system configured to automatically produce neutral domain data, comprising:
A computer-readable memory medium containing instructions for controlling a computer processor, when executed, to automatically produce neutral domain data that is incorporated in a machine learning model by performing a method comprising:
Dependencies are inferred from the claim text.