Intelligent Detection of Overlapping Fiber Anomalies in Optical Networks Using Machine Learning

We propose a machine learning approach leveraging state-of-polarization dynamics to detect overlapping fiber anomalies. Simulated disturbances and XGBoost classification achieve near-perfect accuracy under noise, enabling precise identification of concurrent events and enhancing both fault detection and physical layer security in optical communication networks.