ML-based Anomaly Detection in Optical Fiber Monitoring. (arXiv:2202.11756v1 [cs.CR])

Secure and reliable data communication in optical networks is critical for
high-speed internet. We propose a data driven approach for the anomaly
detection and faults identification in optical networks to diagnose physical
attacks such as fiber breaks and optical tapping. The proposed methods include
an autoencoder-based anomaly detection and an attention-based bidirectional
gated recurrent unit algorithm for the fiber fault identification and
localization. We verify the efficiency of our methods by experiments under
various attack scenarios using real operational data.

anomaly detection detection fiber ml monitoring