Zscaler, Inc.

Sanjit Ganguli

Srikanth Devarajan

Chakkaravarthy Periyasamy Balaiah

Nathan Howe

Chenglong Zheng

Sandeep Kamath Voderbet

Javier Rodriguez Gonzalez

Prasannakumar Jobigenahally Malleshaiah

Alexander Frazier

Ashok Kolachina

Daniel Ballmer

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 are provided for controlling network access in a zero trust environment. A method, according to one implementation, includes the step of monitoring and controlling access between a user device and a network application using a zero trust policy engine having a Zero Trust Architecture (ZTA) in which no user, user device, or network application is inherently trusted. The method further includes the step of granting trust by allowing the user device to access the network application when identity and context information associated with a user of the user device is verified and when policy checks of the zero trust policy engine are enforced.

Zero trust policy engine for controlling access to network applications

Systems and methods for implemented by a user device for Real User Monitoring (RUM) include operating an add on for a web browser; receiving a list of domains or Uniform Resource Locators (URLs) to calculate RUM data thereon; responsive to the web browser accessing any of the domains or URLs in the list, calculating and storing RUM data; and periodically sending the stored RUM data to a cloud-based system. The RUM data can include statistics, metrics, and errors that are detected based on any of start of navigation, redirects, Domain Name System (DBS), connection establishment and teardown, Hypertext Transfer Protocol (HTTP) request and response start and end, Document Object Model (DOM) load time, page load time, and Java Script and AJAX error detection.

Real user monitoring statistics from end users

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Great Falls, VA, United States of America

Sanjit Ganguli