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...

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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
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