The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Machine Learning Analysis of Polarization Signatures for Distinguishing Harmful from Non-harmful Fiber Events
Open accessAbstract
Secure and reliable data transmission in optical networks is essential for supporting high-speed internet services. Optical fibers, the enabler of global connectivity for millions of users, are vulnerable to various potentially harmful events including mechanical failures, like fiber cuts, and malicious physical layer attacks, such as eavesdropping. These incidents can degrade network performance, breach privacy and integrity through unauthorized access to the transmitted data, and cause significant financial and data loss. It is, therefore, crucial to detect and classify the malicious events. Continuous monitoring of polarization state changes, combined with application of machine learning algorithms, enables detection of deviations in the polarization patterns caused by the harmful events. In this study, we introduce a method that detects and identifies potential harmful events in optical networks. By using a Histogram Gradient Boosting classifier within our machine learning framework, we achieve $97.94 \%$ detection accuracy of the harmful and non-harmful events.
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Cites
Comparative Performance Analysis of Hamming, Hanning and Blackman Window
Podder, P., Khan, T. Z., Khan, M. H., & Rahman, M. (2014) · International Journal of Computer Applications · doi:10.5120/16891-6927 · Cited by 231
Proactive Fiber Damage Detection in Real-time Coherent Receiver
Boitier, F., Lemaire, V., Pesic, J., Chavarria, L., Layec, P., Bigo, S., & Dutisseuil, E. (2017) · 2017 European Conference on Optical Communication (ECOC) · doi:10.1109/ecoc.2017.8346077 · Cited by 68
Anomaly Detection and Localization in Optical Networks Using Vision Transformer and SOP Monitoring
Abdelli, K., Lonardi, M., Gripp, J., Correa, D., Olsson, S., Boitier, F., & Layec, P. (2024) · Optical Fiber Communication Conference (OFC) 2024 · doi:10.1364/ofc.2024.tu2j.4 · Cited by 19
Sadighi, L., Karlsson, S., Natalino, C., & Furdek, M. (2024) · Optical Fiber Communication Conference (OFC) 2024 · doi:10.1364/ofc.2024.m1h.1 · Cited by 19
Self-citationEavesdropping G.652 vs. G.657 fibres: a performance comparison
Karlsson, S., Lin, R., Wosinska, L., & Paolo Monti, A. (2022) · International Conference on Optical Network Design and Modeling (ONDM) · doi:10.23919/ondm54585.2022.9782849 · Cited by 19
Self-citationAbdelli, K., Lonardi, M., Gripp, J., Olsson, S., Boitier, F., & Layec, P. (2023) · IET Conference Proceedings · doi:10.1049/icp.2023.2374 · Cited by 13
Proactive restoration of optical links based on the classification of events
Pesic, J., Rouzic, E. L., Brochier, N., & Dupont, L. (2011) · HAL · Cited by 13
Lemaire, V., Boitier, F., Pesic, J., Bondu, A., Ragot, S., & Clérot, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39098-3_3 · Cited by 8
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Deep Learning for Detection of Harmful Events in Real-World, Noisy Optical Fiber Deployments
Sadighi, L., Karlsson, S., Natalino, C., Wosinska, L., Ruffini, M., & Furdek, M. (2025) · Journal of Lightwave Technology · doi:10.1109/jlt.2025.3557748 · Cited by 18
Self-citationMalik, G., Dipto, I. C., Masood, M. U., Mohamed, M. C., Straullu, S., Bhyri, S. K., Galimberti, G. M., Napoli, A., Pedro, J., Wakim, W., et al. (2025) · AI · doi:10.3390/ai6070131 · Cited by 10
Fiber Eavesdropping Detection and Location in Optical Communication System
Li, Y., Liang, Y., Zhang, M., Wei, S., Zhu, H., Li, Y., Zhao, Y., & Zhang, J. (2025) · Photonics · doi:10.3390/photonics12050501 · Cited by 9
Yang, B., Tang, J., Yu, H., Hao, Y., Gao, S., Fan, L., Yao, Y. F., Liang, J., Wei, J., & Yang, Y. (2025) · Journal of Optical Communications and Networking · doi:10.1364/jocn.559810 · Cited by 7
Machine Learning for Predictive Multi-Event Detection in Fiber Optic Systems
Malik, G., Masood, M. U., Mohamed, M. C., Straullu, S., Bhyri, S. K., Galimberti, G. M., Pedro, J., Napoli, A., Wakim, W., & Curri, V. (2025) · IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) · doi:10.1109/icmlcn64995.2025.11140455 · Cited by 7
ML-Based State of Polarization Analysis to Detect Emerging Threats to Optical Fiber Security
Sadighi, L., Karlsson, S., Natalino, C., & Furdek, M. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3607022 · Cited by 6
Self-citationAI-Driven Fault Prediction and Restoration Leveraging Real-Time SOP Monitoring
Malik, G., Dipto, I. C., Masood, M. U., Ali, A., Mohamed, M. C., Straullu, S., Bhyri, S. K., Galimberti, G. M., Pedro, J., Napoli, A., et al. (2025) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom66362.2025.11197358 · Cited by 5
Sadighi, L., Natalino, C., Karlsson, S., Ruffini, M., Kenny, E., Wosinska, L., & Furdek, M. (2025) · European Conference on Optical Communication · doi:10.1109/ecoc66593.2025.11263096 · Cited by 4
Self-citationYang, B., Yu, H., Yan, Y., Zhang, C., Tang, J., Hao, Y., Wei, J., Sun, G., & Yang, Y. (2026) · Journal of Optical Communications and Networking · doi:10.1364/jocn.584770 · Cited by 4
Variational autoencoder domain adaptation for cross-system generalization in ML-based SOP monitoring
Sadighi, L., Karlsson, S., Natalino, C., Eshghie, M., Usmani, F., Kenny, E. M., Wosinska, L., Paolo Monti, A., Furdek, M., & Ruffini, M. (2026) · Journal of Optical Communications and Networking · doi:10.1364/jocn.598996 · Cited by 2
Self-citation(2024) · doi:10.1109/icton67126.2025.11125320 · Cited by 1
Self-citationDP-16QAM Modulated vs. Unmodulated Polarization Signatures for ML-Based Fiber Sensing
Sadighi, L., Natalino, C., Karlsson, S., Wosinska, L., Kenny, E. M., Garbhapu, V. V., Ruffini, M., & Furdek, M. (2026) · Journal of Lightwave Technology · doi:10.1109/jlt.2026.3660791 · Cited by 1
Self-citationQin, W., Gong, X., Hou, W., Gan, L., Xu, Y., & Guo, L. (2026) · Journal of Lightwave Technology · doi:10.1109/jlt.2026.3733656
Metadata
- Authors
- Sadighi, L., Karlsson, S., Wosinska, L., & Furdek, M.
- Affiliations
- Chalmers University of Technology (SE)
- Defence Materiel Administration (SE)
- Venue
- 2024 24th International Conference on Transparent Optical Networks (ICTON)
- Publisher
- IEEE
- Published
- 2024-07-14
- Pages
- 1-5
- OpenAlex
- W4402125296
- Semantic Scholar
- 96daab7eda131227dd3e16e73d60dd939d8b3781
- Cited by
- 13
- References
- 9
- Citations by year
- 2026: 4 · 2025: 9
- FWCI
- 9.93
- Type
- conference paper
- Language
- en
- Open access
- green
- research.chalmers.se
- Locations
- International Conference on Transparent Optical Networks (publishedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Network Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Photonic Crystal and Fiber Optics
- Keywords
- physical layer attacks, eavesdropping detection, fiber cut, histogram gradient boosting, optical network security
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