Cylance Inc.

Stuart McClure

Derek A Soeder

Matthew Wolff

Ryan Permeh

Glenn Chisholm

Gary Golomb

Braden Russell

Gabriel Acevedo

Michael O'Dea

Seagen Levites

Michael O'DEA

Glenn Chrisholm

Under one aspect, a computer-implemented method includes receiving a query at a query interface about whether a computer file comprises malicious code. It is determined, using at least one machine learning sub model corresponding to a type of the computer file, whether the computer file comprises malicious code. Data characterizing the determination are provided to the query interface. Generating the sub model includes receiving computer files at a collection interface. Multiple sub populations of the computer files are generated based on respective types of the computer files, and random training and testing sets are generated from each of the sub populations. At least one sub model for each random training set is generated.

Automated systems and methods for generative multimodel multiclass classification and similarity analysis using machine learning

Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

Application execution control utilizing ensemble machine learning for discernment

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Irvine, CA, United States of America

Stuart McClure