US 12,438,785 B2
Advanced machine learning techniques for internet outage detection
Prasannakumar Jobigenahally Malleshaiah, San Jose, CA (US); Alexander Frazier, San Jose, CA (US); Chakkaravarthy Periyasamy Balaiah, San Jose, CA (US); Javier Rodriguez Gonzalez, San Jose, CA (US); Ashok Kolachina, Milpitas, CA (US); and Sanjit Ganguli, Great Falls, VA (US)
Assigned to Zscaler, Inc., San Jose, CA (US)
Filed by Zscaler, Inc., San Jose, CA (US)
Filed on Jun. 1, 2022, as Appl. No. 17/829,618.
Prior Publication US 2023/0396512 A1, Dec. 7, 2023
Int. Cl. H04L 41/16 (2022.01); H04L 9/40 (2022.01); H04L 41/147 (2022.01); H04L 43/10 (2022.01); H04Q 9/00 (2006.01)
CPC H04L 41/16 (2013.01) [H04L 41/147 (2013.01); H04L 43/10 (2013.01); H04L 63/0227 (2013.01); H04Q 9/00 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory computer-readable medium comprising instructions that, when executed, cause a processor to
receive, at a cloud-based system, telemetry data from a plurality of connector applications associated with a plurality of user devices, wherein the telemetry data is collected at the plurality of connector applications, the telemetry data being relative to performance of all tiers of Internet Service Providers (ISPs) from a point of view of the plurality of user devices;
generate baselines of the performance of the ISPs based on a plurality of metrics and the collected telemetry data, wherein the baselines are generated over a plurality of geo granularities;
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 and determine a severity of the blackout or brownout, wherein the blackout or brownout is identified when real time performance is worse than the performance baselines identified by the model, and wherein the severity of the blackout or brownout is based on a number of users affected and a geographic size of an affected area.