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

This paper presents an experimental proof-of- concept 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 sig- nificant network disruptions. The proposed ML-based approach is able to continuously monitor SOP fluctuations, enabling the early detection of vibrations and proactive mitigation of poten- tial 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.