Machine Learning-based Anomaly Detection in Optical Fiber Monitoring

no code implementations • 19 Mar 2022 • Khouloud Abdelli, JOO YEON CHO, Florian Azendorf, Helmut Griesser, Carsten Tropschug, Stephan Pachnicke

The proposed method combines an autoencoder-based anomaly detection and an attention-based bidirectional gated recurrent unit algorithm, whereby the former is used for fault detection and the latter is adopted for fault diagnosis and localization once an anomaly is detected by the autoencoder.

Anomaly Detection BIG-bench Machine Learning +1

ML-based Anomaly Detection in Optical Fiber Monitoring

no code implementations • 23 Feb 2022 • Khouloud Abdelli, JOO YEON CHO, Carsten Tropschug

Secure and reliable data communication in optical networks is critical for high-speed internet.

Anomaly Detection

Predictive Maintenance for Optical Networks in Robust Collaborative Learning

no code implementations • 29 Sep 2021 • Khouloud Abdelli, JOO YEON CHO

Federated learning (FL) is a promising candidate to tackle the aforementioned challenge by enabling the development of a global ML model using datasets owned by many vendors without revealing their business-confidential data.

Federated Learning