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US Patent 11783035 Multi-representational learning models for static analysis of source code

Patent 11783035 was granted and assigned to Palo Alto Networks on October, 2023 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Palo Alto Networks
Palo Alto Networks
Current Assignee
Palo Alto Networks
Palo Alto Networks
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11783035
Patent Inventor Names
Fang Liu
Brody James Kutt
Oleksii Starov
Yuchen Zhou
William Redington Hewlett, II
Date of Patent
October 10, 2023
Patent Application Number
17987729
Date Filed
November 15, 2022
Patent Citations
‌
US Patent 10846401 System, method and apparatus for usable code-level statistical analysis with applications in malware detection
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US Patent 10846403 Detecting malicious executable files by performing static analysis on executable files' overlay
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US Patent 11005860 Method and system for efficient cybersecurity analysis of endpoint events
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US Patent 11200053 Deployment models
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US Patent 11366680 Cloud native virtual machine runtime protection
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US Patent 11379577 Uniform resource locator security analysis using malice patterns
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US Patent 11568055 System and method for automatically detecting a security vulnerability in a source code using a machine learning model
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US Patent 11550911 Multi-representational learning models for static analysis of source code
...
Patent Primary Examiner
‌
Hosuk Song
CPC Code
‌
G06F 8/42
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G06F 21/565
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G06N 20/00
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G06F 21/563
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G06F 21/564
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G06F 8/75
Patent 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 receiving at 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 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 sample and without performing dynamic analysis of the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.

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