The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 01, 2023

Filed:

Jul. 29, 2022
Applicant:

Palo Alto Networks, Inc., Santa Clara, CA (US);

Inventors:

Yanhui Jia, San Jose, CA (US);

Matthew W. Tennis, Santa Clara, CA (US);

Stefan Achleitner, Arlington, VA (US);

Taojie Wang, San Jose, CA (US);

Hui Gao, Sunnyvale, CA (US);

Shengming Xu, San Jose, CA (US);

Assignee:

Palo Alto Networks, Inc., Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 21/56 (2013.01); G06F 21/53 (2013.01); G06N 5/022 (2023.01);
U.S. Cl.
CPC ...
G06F 21/56 (2013.01); G06F 21/53 (2013.01); G06N 5/022 (2013.01); G06F 2221/031 (2013.01);
Abstract

Techniques for sample traffic based self-learning malware detection are disclosed. In some embodiments, a system/process/computer program product for sample traffic based self-learning malware detection includes receiving a plurality of samples for malware detection analysis using a sandbox; executing each of the plurality of samples in the sandbox and monitoring network traffic during execution of each of the plurality of samples in the sandbox; detecting that one or more of the plurality of samples is malware based on automated analysis of the monitored network traffic using a command and control (C2) machine learning (ML) model if there is not a prior match with an intrusion prevention system (IPS) signature; and performing an action in response to detecting that the one or more of the plurality of samples is malware based on the automated analysis of the monitored network traffic using the C2 ML model. In some embodiments, the IPS signatures and C2 ML model are automatically generated and trained.


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