Patent · US Active

Utilizing Machine Learning to detect malicious executable files efficiently and effectively

US11803641B2 · kind B2 · utility

1Cited by

6References

20Claims

0Family size

Assignee

Zscaler, Inc. · US

Inventors

Changsha Ma · Campbell, US
Nirmal Singh · Temple, US
Naveen Selvan · Mohali, IN
Tarun Dewan · Mohali, IN
Uday Pratap Singh · Mohali, IN
Deepen Desai · San Ramon, US
Bharath Meesala · Bengaluru, IN
Rakshitha Hedge · Bengaluru, IN
Parnit Sainion · معلمی نژاد, US
Shashank Gupta · Sammamish, US
Narinder Paul · Sunnyvale, US
Rex Shang · Los Altos, US
Howie Xu · Palo Alto, US

Key dates

Filing date	Oct 26, 2020
Grant date	Oct 31, 2023
Priority date	—
Expiry date	Oct 16, 2041

Classification

Technology area (CPC G)Physics
CPC primaryG06F2221/033
WIPO fieldComputer technology
WIPO sectorElectrical engineering

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.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.