Invention Grant
- Patent Title: Building multi-representational learning models for static analysis of source code
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Application No.: US16779271Application Date: 2020-01-31
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Publication No.: US11615184B2Publication Date: 2023-03-28
- Inventor: Brody James Kutt , William Redington Hewlett, II , Oleksii Starov , Yuchen Zhou , Fang Liu
- Applicant: Palo Alto Networks, Inc.
- Applicant Address: US CA Santa Clara
- Assignee: Palo Alto Networks, Inc.
- Current Assignee: Palo Alto Networks, Inc.
- Current Assignee Address: US CA Santa Clara
- Agency: Van Pelt, Yi & James LLP
- Main IPC: G06F9/44
- IPC: G06F9/44 ; G06F9/455 ; G06F21/56 ; G06N20/00 ; G06F8/41 ; G06F8/75 ; G06K9/62

Abstract:
A system/process/computer program product for building multi-representational learning models for static analysis of source code includes receiving training data, wherein the training data includes a set of source code files for training a multi-representational learning (MRL) model for classifying malicious source code and benign source code based on a static analysis; generating a first feature vector based on a set of characters extracted from the set of source code files; generating a second feature vector based on a set of tokens extracted from the set of source code files; and performing an ensemble of the first feature vector and the second feature vector to form a target feature vector for classifying malicious source code and benign source code based on the static analysis.
Public/Granted literature
- US20210240826A1 BUILDING MULTI-REPRESENTATIONAL LEARNING MODELS FOR STATIC ANALYSIS OF SOURCE CODE Public/Granted day:2021-08-05
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