Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning
Artikel i vetenskaplig tidskrift, 2021

Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, we propose an optical spectrum anomaly detection scheme that exploits computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals. The proposed scheme achieves 100% detection accuracy even without prior knowledge of the anomalies. Furthermore, operation with encoded images of constellation diagrams reduces the runtime by up to 200 times.

Författare

Carlos Natalino Da Silva

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Aleksejs Udalcovs

RISE Research Institutes of Sweden

Lena Wosinska

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Oskars Ozolins

RISE Research Institutes of Sweden

Marija Furdek Prekratic

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

IEEE Communications Letters

1089-7798 (ISSN)

Vol. In Press

Skydda optiska kommunikationsnätverk från cyber-säkerhetsattacker

Vetenskapsrådet (VR), 2020-01-01 -- 2023-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Telekommunikation

Kommunikationssystem

Signalbehandling

DOI

10.1109/LCOMM.2021.3055064

Relaterade dataset

CONSTELLATION DIAGRAMS FOR SPECTRUM ANOMALY DETECTION IN OPTICAL NETWORKS [dataset]

URI: https://ieee-dataport.org/open-access/constellation-diagrams-spectrum-anomaly-detection-optical-networks DOI: 10.21227/g9s9-ba02

Mer information

Senast uppdaterat

2021-02-25