Palo Alto Networks, Inc.

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Wenjun Hu

William Redington Hewlett, Ii

Zihang Xiao

Hao Huang

Daniel Raygoza

Min Du

Esmid Idrizovic

Yang Ji

Anirudh Mittal

Ashwin Kumar Dewan

Curtis Leland Carmony

Jeff White

A set of features including a first feature and a second feature is received at a server. A subset of the set of features is determined for use in generating a model usable by a device to locally make a malware classification decision. The device has reduced computing resources as compared to computing resources of the server. The subset of the set of features is used to generate the model. The generated model includes the first feature and does not include the second feature. A determination is made, at a time subsequent to the generation of the model, that an updated model should be deployed to the device. An updated model is generated.

Specific file detection baked into machine learning pipelines

The detection of phishing Portable Document Format (PDF) files using an image-based deep learning approach is disclosed. A PDF document that includes a Universal Resource Locator is received. A likelihood that the received PDF document represents a phishing threat is determined, at least in part, by using an image based model. A verdict for the PDF document is provided as output based at least in part on the determined likelihood.

Detecting phishing PDFs with an image-based deep learning approach

Automatic generation of a malware signature is disclosed. Code of a sample including packages and function names is parsed. Standard type packages and vendor type packages are filtered from the code of the sample to obtain main type packages. A signature using a fuzzy hash for the sample is generated based on the main type packages. A determination of whether the sample is malware is performed using the signature and a similarity score threshold.

Method and system for automatically generating malware signature

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Carmel, IN, United States of America

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