Zscaler, Inc.

Visvanathan Thothathri

Narinder Paul

Shashank Gupta

Howie Xu

Rex Shang

Dianhuan Lin

Changsha Ma

Parnit Sainion

Douglas A Koch

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 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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Santa Clara, CA, United States of America

Visvanathan Thothathri