ML-Based Detection and Categorization of Complex Mechanical Vibrations via State of Polarization Analysis in Optical Networks

Paper i proceeding, 2025

Modern optical networks form the critical backbone of global communications, enabling high-speed data transmission for a wide range of applications. Despite their inherent advantages in bandwidth and scalability, these networks are not immune to physical-layer vulnerabilities. Mechanical disturbances, both accidental and intentional, can compromise service quality or serve as gateways for more severe cyber-physical attacks. Thus, there is a growing need for intelligent, real-time monitoring solutions capable of detecting and interpreting subtle anomalies in optical fiber infrastructures. This paper presents a Machine Learning (ML)-based State of Polarization (SOP) monitoring approach for the identification and classification of complex mechanical vibrations in optical fiber networks. We address the real-world challenge of mixed-frequency and overlapping vibration signatures, arising from benign activities, malicious attacks, or simultaneous events, by collecting 14 distinct polarization signatures under various physical scenarios. A diverse set of supervised ML classifiers is evaluated, with Histogram Gradient Boosting (HGB) achieving the highest performance at 88.33% accuracy.