Network anomaly detection using machine learning models

Abstract

Anomalies in network traffic are detected using machine learning. A plurality of machine learning models is employed to determine whether there are anomalies in network traffic of an MPLS (Multiprotocol Label Switching) network that can affect the performance of devices in the network. A first machine learning model is trained on network traffic passed through network tunnels of a plurality of routers in the network. A second machine learning model is trained on router-specific network traffic passed through router-specific network traffic for a subset of the network tunnels associated with a particular router. The first machine learning model is employed to determine a network anomaly, and the second machine learning model is employed to determine a router-specific anomaly. A router error is identified when both a network anomaly and a router-specific anomaly are determined. An indication of the router error is communicated to a computing device.