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Anomaly detection in a network based on a key performance indicator prediction model

專利號(hào)
US11616707B2
公開日期
2023-03-28
申請(qǐng)人
VIAVI Solutions Inc.(US AZ Chandler)
發(fā)明人
Dave Padfield; Yannis Petalas; Oliver Parry-Evans
IPC分類
H04L43/0823; H04L43/04; H04L43/16
技術(shù)領(lǐng)域
kpi,prediction,network,anomaly,platform,or,monitoring,value,may,threshold
地域: CA CA San Jose

摘要

A network monitoring platform may obtain a measurement of a particular value of a key performance indicator (KPI) and one or more parameters of the particular value of the KPI. The network monitoring platform may determine a prediction of the particular value of the KPI. The network monitoring platform may determine an amount of error in the prediction of the particular value of the KPI, wherein the amount of error in the prediction of the particular value of the KPI is based on a difference between the prediction of the particular value of the KPI and the measurement of the particular value of the KPI. The network monitoring platform may perform, based on the amount of error in the prediction of the particular value of the KPI, one or more actions.

說明書

FIG. 5 is a flow chart of an example process 500 for anomaly detection in a network. In some implementations, one or more process blocks of FIG. 5 may be performed by a network monitoring platform (e.g., network monitoring platform 240). In some implementations, one or more process blocks of FIG. 5 may be performed by another device or a group of devices separate from or including the network monitoring platform, such as an endpoint at site 1 (e.g., site 1 endpoint 210), an endpoint at site 2 (e.g., site 2 endpoint 220), a network node (e.g., network node 230), and a computing resource (e.g., computing resource 245), and/or the like.

As shown in FIG. 5, process 500 may include training a KPI prediction model, based on measurements of historical values of a KPI and associated parameters, using one or more machine learning processes (block 510). For example, the network monitoring platform (e.g., using computing resource 245, processor 320, memory 330, storage component 340, input component 350, output component 360, communication interface 370 and/or the like) may train a KPI prediction model, based on measurements of historical values of a KPI and associated parameters, using one or more machine learning processes, as described above.

權(quán)利要求

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