Resilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring

Resilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical...

Full description

Saved in:
Bibliographic Details
Main Authors: Gulmina Malik, Imran Chowdhury Dipto, Muhammad Umar Masood, Mashboob Cheruvakkadu Mohamed, Stefano Straullu, Sai Kishore Bhyri, Gabriele Maria Galimberti, Antonio Napoli, João Pedro, Walid Wakim, Vittorio Curri
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:AI
Subjects:
state of polarization
machine learning
random forest
XGBoost
decision tree
k-NN
Online Access:https://www.mdpi.com/2673-2688/6/7/131
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks.
ISSN:2673-2688

Similar Items