Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning
Journal article, 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.

constellation diagram

anomaly detection

Deep unsupervised learning

autoencoder

optical network monitoring

Author

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

Aleksejs Udalcovs

RISE Research Institutes of Sweden

Lena Wosinska

Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

Oskars Ozolins

RISE Research Institutes of Sweden

Marija Furdek Prekratic

Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

IEEE Communications Letters

1089-7798 (ISSN)

Vol. 25 5 1583-1586 9336677

Safeguarding optical communication networks from cyber-security attacks

Swedish Research Council (VR) (2019-05008), 2020-01-01 -- 2023-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/LCOMM.2021.3055064

Related datasets

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

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

More information

Latest update

6/11/2021