Machine Learning-Based Polarization Signature Analysis for Detection and Categorization of Eavesdropping and Harmful Events

Abstract

We propose a methodology that uses polarization state changes and machine learning to detect and classify eavesdropping, harmful, and non-harmful events in the optical fiber network. Our solution achieves 92.3% accuracy over 13 experimental scenarios.