Invention Grant
- Patent Title: Multi-representational learning models for static analysis of source code
-
Application No.: US16779268Application Date: 2020-01-31
-
Publication No.: US11550911B2Publication Date: 2023-01-10
- 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: G06F21/56
- IPC: G06F21/56 ; G06F8/41 ; G06F8/75 ; G06N20/00

Abstract:
Techniques for multi-representational learning models for static analysis of source code are disclosed. In some embodiments, a system/process/computer program product for multi-representational learning models for static analysis of source code includes storing on a networked device a set comprising one or more multi-representation learning (MRL) models for static analysis of source code; performing a static analysis of source code associated with a sample received at the network device, wherein performing the static analysis includes using at least one stored MRL model; and determining that the sample is malicious based at least in part on the static analysis of the source code associated with the received sample, and in response to determining that the sample is malicious, performing an action based on a security policy.
Public/Granted literature
- US20210240825A1 MULTI-REPRESENTATIONAL LEARNING MODELS FOR STATIC ANALYSIS OF SOURCE CODE Public/Granted day:2021-08-05
Information query