ML-based State of Polarization Analysis to Detect Emerging Threats to Optical Fiber Security
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| Titel: | ML-based State of Polarization Analysis to Detect Emerging Threats to Optical Fiber Security |
|---|---|
| Autoren: | Sadighi, Leyla, 1989, Karlsson, Stefan, Natalino Da Silva, Carlos, 1987, Furdek Prekratic, Marija, 1985 |
| Quelle: | IEEE Transactions on Network and Service Management. In Press |
| Schlagwörter: | State of Polarization (SOP) variations, Semi-Supervised Learning (SSL), anomaly detection, Machine Learning (ML), Unsupervised Learning (USL), One-Class Support Vector Machine (OCSVM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) |
| Beschreibung: | As the foundation of global communication networks, optical fibers are vulnerable to various disruptive events, including mechanical damage, such as cuts, and malicious physical layer breaches, such as eavesdropping via fiber bending. Traditional monitoring methods often fail to identify subtle or novel anomalies, stimulating the proliferation of Machine Learning (ML) techniques for detection of threats before they cause significant harm. In this paper, we evaluate the performance of Semi-Supervised Learning (SSL) and Unsupervised Learning (USL) approaches for detecting various abnormal events, such as fiber bending and vibrations, by analyzing polarization signatures with minimal reliance on labeled data. We experimentally collect thirteen polarization signatures on three different types of fiber cable and process them using One-Class Support Vector Machine (OCSVM) as an SSL, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) as a USL algorithm for anomaly detection. We introduce tailored evaluation metrics designed to guide hyper-parameter tuning and capture generalization over different anomaly types, detection consistency, and robustness to false positives, enabling practical deployment of OCSVM and DBSCAN in optical fiber security. Our findings demonstrate DBSCAN as a strong contender to detect previously unseen threats in scenarios where labeled data are not available, despite some variability in performance between different scenarios, with F1 score values between 0.615 and 0.995. In contrast, OCSVM, trained on normal operating conditions, maintains high F1 scores of 0.98 to 0.998, demonstrating accurate detection of complex anomalies in optical networks. |
| Dateibeschreibung: | electronic |
| Zugangs-URL: | https://research.chalmers.se/publication/548341 https://research.chalmers.se/publication/548341/file/548341_Fulltext.pdf |
| Datenbank: | SwePub |
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