Palo Alto Networks, Inc.

Min Du

William Redington Hewlett, Ii

Wenjun Hu

Hao Huang

Brody James Kutt

Daniel Raygoza

Sheng Yang

Tyler Pals Halfpop

Esmid Idrizovic

Nandini Ramanan

Sujit Rokka Chhetri

Shan Huang

Yijie Sui

Jeff White

Curtis Leland Carmony

Techniques for using deep learning to identify malicious image files are disclosed. A plurality of sections of a first image are received. The received sections are used to determine a likelihood that the first image is malicious. The determination is made, at least in part, using a model trained using a set of sections extracted from a set of sample images. A verdict is provided for the first image.

Deep learning for malicious image file detection

The present application discloses a method, system, and computer system for classifying stream data at an edge device. The method includes obtaining a stream of a file at the edge device, aligning a predetermined amount of data in chunks associated with the stream of the file, processing a plurality of aligned chunks associated with the stream of the file using a machine learning model, and classifying, at the edge device, the file based at least in part on a classification of the plurality of aligned chunks.

Machine learning architecture for detecting malicious files using stream of data

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

Malware signature generation through combination rule mining is disclosed. A set of properties associated, collectively, with a plurality of data samples is received. A first data sample has a first set of properties and a second data sample has a second set of properties. A combination signature comprising at least a first property included in the first set of properties and a second property included in the second set of properties is generated.

Combination rule mining for malware signature generation

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Santa Clara, CA, United States of America

Min Du