Container file analysis using machine learning model

US10637874B2 · kind B2 · utility

Assignee

• Cylance Inc. · US

Inventors

• Xuan Zhao · Irvine, US
• Matthew Wolff · Laguna Niguel, US
• John Brock · Irvine, US
• Brian Wallace · Irvine, US
• Andrew Wortman · Irvine, US
• Jian Luan · Irvine, US
• Mahdi Azarafrooz · Irvine, US
• Andrew Davis · Williston, US
• Michael Wojnowicz · Irvine, US
• Derek A. Soeder · Irvine, US
• David Beveridge · Hillsboro, US
• Yaroslav Oliinyk · Portland, US
• Ryan Permeh · Laguna Hills, US

Key dates

• Filing date: Nov 7, 2016
• Grant date: Apr 28, 2020
• Expiry date: Jan 28, 2037

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/10
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.