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US Patent 11550911 Multi-representational learning models for static analysis of source code

Patent 11550911 was granted and assigned to Palo Alto Networks on January, 2023 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Palo Alto Networks
Palo Alto Networks
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Current Assignee
Palo Alto Networks
Palo Alto Networks
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
115509110
Patent Inventor Names
Oleksii Starov0
Fang Liu0
Brody James Kutt0
Yuchen Zhou0
William Redington Hewlett, II0
Date of Patent
January 10, 2023
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Patent Application Number
167792680
Date Filed
January 31, 2020
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Patent Citations
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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 10515002 Utilizing artificial intelligence to test cloud applications
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US Patent 10754947 System, method and apparatus for usable code-level statistical analysis with applications in malware detection
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US Patent 10803166 Automated determination of application privileges
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US Patent 10846401 System, method and apparatus for usable code-level statistical analysis with applications in malware detection
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US Patent 11005860 Method and system for efficient cybersecurity analysis of endpoint events
Patent Citations Received
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US Patent 11790085 Apparatus for detecting unknown malware using variable opcode sequence and method using the same
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US Patent 12063248 Deep learning for malicious URL classification (URLC) with the innocent until proven guilty (IUPG) learning framework
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US Patent 11856003 Innocent until proven guilty (IUPG): adversary resistant and false positive resistant deep learning models
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US Patent 11783035 Multi-representational learning models for static analysis of source code
Patent Primary Examiner
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Hosuk Song
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CPC Code
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G06F 8/75
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G06F 8/42
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G06F 21/565
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G06F 21/564
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G06F 21/563
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G06N 20/00
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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.

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