Zscaler, Inc.

Parnit Sainion

Narinder Paul

Shashank Gupta

Howie Xu

Rex Shang

Changsha Ma

Deepen Desai

Dianhuan Lin

Douglas A Koch

Tarun Dewan

Rakshitha Hedge

Visvanathan Thothathri

Bharath Kumar

Bharath Meesala

Uday Pratap Singh

Naveen Selvan

Nirmal Singh

Kripesh Dhakal

Amit Jain

Sumukh Radhakrishna Rao

Yaamika Dedhia

Vinay Polurouthu

Systems and methods include responsive to a user attempting to access content in a cloud-based system, obtaining the content associated with the user; sending the content to a sandbox for processing; rendering the content within an isolated browser, thereby allowing the user to interact with the content during the processing; and receiving a verdict from the sandbox, wherein the verdict labels the content as one of malicious, benign, and unknown, and performing an action based thereon.

Systems and methods for cloud sandboxing with browser isolation for immediate content access

Systems and methods of sandboxing a file include responsive to receiving a file associated with a user, obtaining policy for the user; analyzing the file with a machine learning model; and based on a combination of the policy for the user and a verdict of the machine learning model, one of quarantining the file for analysis in a sandbox and allowing the file to the user. The present disclosure presents a smart quarantine with a goal of minimizing the number of files quarantined, the number of malicious files passed through to an end user, and a number of files scanned by a sandbox.

Utilizing machine learning for smart quarantining of potentially malicious files

Systems and methods include performing inline monitoring of production traffic between users, the Internet, and cloud services via a cloud-based system; utilizing a trained machine learning model to inspect static properties of files in the production traffic; and classifying the traffic as one of malicious or benign based on the trained machine learning model.

Utilizing machine learning to detect malicious executable files efficiently and effectively

Systems and methods include receiving a trained machine learning model that has been processed with training information removed therefrom, wherein the training information is utilized in training of the trained machine learning model; monitoring traffic, inline at the node, including processing the traffic with the trained machine learning model; obtaining a verdict on the traffic based on the trained machine learning model; and performing an action on the traffic based on the verdict.

Machine learning model abstraction layer for runtime efficiency

Systems and methods include determining a plurality of features associated with executable files, wherein the plurality of features are each based on static properties in predefined structure of the executable files; obtaining training data that includes samples of benign executable files and malicious executable files; extracting the plurality of features from the training data; and utilizing the extracted plurality of features to train a machine learning model to detect malicious executable files.

Utilizing Machine Learning to detect malicious executable files efficiently and effectively

Systems and methods include obtaining a file associated with a user for processing; utilizing a combination of policy for the user and machine learning to determine whether to i) quarantine the file and scan the file in a sandbox, ii) allow the file to the user and scan the file in the sandbox, and iii) allow the file to the user without the scan; responsive to the quarantine of the file and the sandbox determining the file is malicious, blocking the file; and, responsive to the quarantine of the file and the sandbox determining the file is benign, allowing the file.

Systems and methods include training a machine learning model with data for identifying features in monitored traffic in a network; analyzing the trained machine learning model to identify information overhead therein, wherein the information overhead is utilized in part for the training; removing the information overhead in the machine learning model; and providing the machine learning model for runtime use for identifying the features in the monitored traffic, with the removed information overhead from the machine learning model.

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Morgan Hill, CA, United States of America

Parnit Sainion