Zscaler, Inc.

Kevin Guo

Rex Shang

Howie Xu

Dianhuan Lin

Changsha Ma

Systems and methods include receiving content for classification; classifying the content as one of benign and malicious utilizing a model that has been trained with a training set of data including benign data and malicious data; determining a first pattern associated with the content; comparing the first pattern with a second pattern that is associated with one of the benign data and the malicious data; and determining an uncertainty of the classifying based on a distance between the first pattern and the second pattern. The systems and methods can include discarding the classification if the distance is greater than a configurable threshold.

Pattern similarity measures to quantify uncertainty in malware classification

Systems and methods include receiving a content item between a user device and a location on the Internet or an enterprise network; utilizing a trained machine learning ensemble model to determine whether the content item is malicious; responsive to the trained machine learning ensemble model determining the content item is malicious or determining the content item is benign but such determining is in a blind spot of the trained ensemble model, performing further processing on the content item; and, responsive to the trained machine learning ensemble model determining the content item is benign with such determination not in a blind spot of the trained machine learning ensemble model, allowing the content item. A blind spot is a location where the trained machine learning ensemble model has not seen any examples with a combination of features at the location or has examples with conflicting labels.

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Milpitas, CA, United States of America

Kevin Guo