Zscaler, Inc.

Alexander Frazier

Chakkaravarthy Periyasamy Balaiah

Dianhuan Lin

Sanjit Ganguli

Javier Rodriguez Gonzalez

Prasannakumar Jobigenahally Malleshaiah

Amanda Carter

Piyush Gour

Ashok Kolachina

Amir Levy

The present systems and methods provide a user performance monitoring solution that enables the monitoring of application and device performance from the end user's point of view. The present systems and methods help Information Technology (IT) personnel to ensure the quality of digital experience across the enterprise. The present system is adapted to collect telemetry data from devices relative to the performance of all tiers of Internet Service Providers (ISPs), create a baseline of the performance of the ISPs based on a plurality of metrics and the collected telemetry data, train a Machine Learning (ML) model to assess blackout and brownout prediction accuracy at different performance values for the metrics, and identify a blackout or brownout, wherein a blackout or brownout is identified when real time performance is worse than the performance values identified by the model.

Advanced machine learning techniques for internet outage detection

Systems and methods include obtaining historical data of traffic for a plurality of locations for a cloud service; labeling the historical data as one of human and server based on a plurality of features; and utilizing the labeled historical data to train a machine learning model to classify traffic as one of human and server. The steps can further include utilizing the trained machine learning model to classify unauthenticated traffic, for the cloud service, from a specific location or a specific IP address.

Network traffic identification using machine learning

Growing community of inventors

San Jose, CA, United States of America

Alexander Frazier