Utilizing a State of Polarization Change Detector and Machine Learning for Enhanced Security in Fiber-Optic Networks

The paper presents a novel method for securing fiber-optic infrastructures using a state of polarization analyzer combined with machine learning algorithms. The proposed system detects vibrations indicative of security breaches, achieving an F1-score above 95.65 %.