Anomaly detection and troubleshooting system for a network using machine learning and/or artificial intelligence

US11522888B2 · kind B2 · utility

Assignee

• NEC CORPORATION · JP

Inventors

• Josep Xavier Salvat Lozano · Heidelberg, DE
• Andres Garcia-Saavedra · Heidelberg, DE
• Xi Li · New York, US
• Xavier Costa Perez · Heidelberg, DE

Key dates

• Filing date: Jul 2, 2019
• Grant date: Dec 6, 2022
• Expiry date: Oct 5, 2041

Classification

• Technology area (CPC H): Electricity
• CPC primary: H04L63/1433
• WIPO field: Digital communication
• WIPO sector: Electrical engineering

Abstract

A method for anomaly detection and troubleshooting in a network includes parsing a network service descriptor (NSD) describing a network service (NS) to be deployed in the network. Monitoring data including time series of service-level metrics and resource-level metrics of network functions (NFs) of the NS are received from different domains of the network. Representations of the time series from the different domains are learned with a common dimensionality. An NS signature of the NS is computed as a cross-correlation matrix comprising cross-correlations between the service-level metrics and the resource-level metrics of the NFs. Embeddings of the NS signature are learned using a model and determining a reconstruction error of the model. It is determined whether the NS is anomalous based on the reconstruction error of the model. The NS is identified as a target for the troubleshooting in a case that the NS was determined to be anomalous.