Machine Learning–Based Detection and Classification of Overlapping Fiber Anomalies

This work presents a machine-learning framework for detecting and classifying independent and overlapping anomaly signatures in optical fiber infrastructure using state-of-polarization (SOP) dynamics. The framework exploits temporal variations of the Stokes parameters to characterize bending, tapping, shaking, and their simultaneous combinations. An experimentally collected dataset is used to evaluate multiple machine-learning models under realistic noise conditions representative of practical fiber monitoring environments. The results demonstrate that SOP-based features enable accurate identification of complex fiber disturbances, strengthening physical-layer monitoring and supporting the secure and reliable operation of next-generation optical networks