Zscaler, Inc.
Utilizing Machine Learning to detect malicious executable files efficiently and effectively
Last updated:
Abstract:
Systems and methods include determining a plurality of features associated with executable files, wherein the plurality of features are each based on static properties in predefined structure of the executable files; obtaining training data that includes samples of benign executable files and malicious executable files; extracting the plurality of features from the training data; and utilizing the extracted plurality of features to train a machine learning model to detect malicious executable files.
Status:
Application
Type:
Utility
Filling date:
26 Oct 2020
Issue date:
17 Mar 2022