Dynamic inspection of networking dependencies to enhance anomaly detection models in a network assurance service

Abstract

In one embodiment, a network assurance service that monitors a network detects, using a machine learning-based anomaly detector, network anomalies associated with source nodes in the monitored network. The network assurance service identifies, for each of the detected anomalies, a set of network paths between the source nodes associated with the anomaly and one or more potential destinations of traffic for that source node. The network assurance service correlates networking devices along the network paths in the identified sets of network paths with the detected network anomalies. The network assurance service adjusts the machine learning-based anomaly detector to use a performance measurement for a particular one of the networking devices as an input feature, based on the correlation between the particular networking device and the detected network anomalies.