  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.
