SOP-Based Anomaly Detection Leveraging Machine Learning for Proactive Optical Restoration

This paper presents an experimental proof-ofconcept for detecting malicious mechanical vibrations in optical
networks using Machine Learning (ML) techniques. The study
leverages the State of Polarization (SOP) as a real-time sensing
mechanism for the identification of anomalous disturbances, like
those caused by drilling, which can lead to fiber cuts and significant network disruptions. The proposed ML-based approach
is able to continuously monitor SOP fluctuations, enabling the
early detection of vibrations and proactive mitigation of potential network failures. By leveraging advanced ML algorithms,
the model effectively identifies between normal environmental
vibrations and critical, harmful disturbances. This capability
ensures timely intervention to protect the network infrastructure.
The ML model achieved a vibration detection accuracy of 95%,
which demonstrates it’s high reliability in distinguishing benign
anomalies from disruptive anomalies. This level of precision
significantly enhances the stability, resilience, and operational
efficiency of the optical network. This leads to a reduction in
the likelihood of service outages and physical infrastructure
damage. The results show the potential of combining real-time
SOP monitoring with ML-based analytics to advance network
management strategies