Vittorio Curri
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Vittorio Curri has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Antonio Napoli
Hardware and Engineering, Nokia, Munich, Germany
1 shared publication - Gabriele Galimberti
Nokia, Optical Networks, Milano, Italy
1 shared publication - Gulmina Malik
Politecnico di Torino, Turin, Italy
1 shared publication - Imran Chowdhury Dipto
Polytechnic University of Turin, Italy
1 shared publication - João Pedro
Instituto de Telecomunicaes, Instituto Superior Tcnico, Lisbon, Portugal
1 shared publication - Mashboob Cheruvakkadu Mohamed
Politecnico di Torino, Turin, Italy
1 shared publication
Latest Publications
- Open Access
- 27 May 2025
- Published by MDPI AG in Preprints.org
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 disturbances, and (iii) malicious fiber tapping (eavesdropping). We have used various supervised machine learning techniques like k-Nearest Neighbor (kNN), 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.
- 27 May 2025
- Published by MDPI AG in Preprints.org
Open AccessResilient 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 disturbances, and (iii) malicious fiber tapping (eavesdropping). We have used various supervised machine learning techniques like k-Nearest Neighbor (kNN), 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.