The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Machine-learning-based method for fiber-bending eavesdropping detection
Abstract
In this Letter, we present a scheme for detecting fiber-bending eavesdropping based on feature extraction and machine learning (ML). First, 5-dimensional features from the time-domain signal are extracted from the optical signal, and then a long short-term memory (LSTM) network is applied for eavesdropping and normal event classification. Experimental data are collected from a 60 km single-mode fiber transmission link with eavesdropping implemented by a clip-on coupler. Results show that the proposed scheme achieves a 95.83% detection accuracy. Furthermore, since the scheme focuses on the time-domain waveform of the received optical signal, additional devices and a special link design are not required.
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In the citing paper
“The communication system may undergo a minor power reduction [37,38] and a bending-induced change in SOP [39], both of which can be detected by an SOP detection system.”
From Resilient Anomaly Detection in Fiber-Optic Networks: A Machine Learning Framework for Multi-Threat Identification Using State-of-Polarization Monitoring (2025) · cites this paper as [37]
In the citing paper
“One of the consequences for the communication system is a small received power drop [48,49], in parallel with a bending-induced SOP variation [50], both of which can be detected by a SOP-sensing system.”
From Overview on the state of polarization sensing: application scenarios and anomaly detection algorithms (2025) · cites this paper as [48]
Citations
Cites
Optical Fiber Communication Conference（OFC '94）
藤本暢宏 (1994) · Cited by 621
Optical Layer Security in Fiber-Optic Networks
Fok, M. P., Wang, Z., Deng, Y., et al. (2011) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2011.2141990 · Cited by 372
Physical-layer security in evolving optical networks
Skorin‐Kapov, N., Furdek, M., Zsigmond, S., et al. (2016) · IEEE Communications Magazine · doi:10.1109/mcom.2016.7537185 · Cited by 282
2021 European Conference on Optical Communication (ECOC)
(2021) · doi:10.1109/ecoc52684.2021 · Cited by 269
2018 European Conference on Optical Communication (ECOC)
(2018) · doi:10.1109/ecoc43462.2018 · Cited by 211
Machine-learning-based anomaly detection in optical fiber monitoring
Abdelli, K., Cho, J. Y., Azendorf, F., Griesser, H., Tropschug, C., & Pachnicke, S. (2022) · Journal of Optical Communications and Networking · doi:10.1364/jocn.451289 · Cited by 149
Optical fiber tapping: Methods and precautions
Zafar Iqbal, M., Fathallah, H., & Belhadj, N. (2011) · 8th International Conference on High-capacity Optical Networks and Emerging Technologies · doi:10.1109/honet.2011.6149809 · Cited by 77
Furdek, M., Natalino, C., Giglio, A. D., & Schiano, M. (2020) · Journal of Optical Communications and Networking · doi:10.1364/jocn.402884 · Cited by 67
Huang, X., Wang, B.-Y., Liu, K., et al. (2020) · Journal of Lightwave Technology · doi:10.1109/jlt.2020.3003396 · Cited by 50
Tanaka, T., Inui, T., Kawai, S., Kuwabara, S., & Nishizawa, H. (2021) · Journal of Optical Communications and Networking · doi:10.1364/jocn.424428 · Cited by 38
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Research works printed but not linked: 2
- Abdelli, 2021, European Conference on Optical Communication (ECOC), 1
- Kingma D. Ba J. , “ Adam: a method for stochastic optimization ,” arXiv , arXiv:1412.6980 ( 2014 ). · arXiv
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Pellegrini, S., Minelli, L., Andrenacci, L., Rizzelli, G., Pilori, D., Bosco, G., Della Chiesa, L., Crognale, C., Piciaccia, S., & Gaudino, R. (2025) · Journal of Optical Communications and Networking · doi:10.1364/jocn.537881 · Cited by 38
Malik, 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
Metadata
- Authors
- Song, H., Lin, R., Li, Y., Lei, Q., Zhao, Y., Wosinska, L., Monti, P., & Zhang, J.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Chalmers University of Technology (SE)
- Chalmers University of Technology
- Corresponding author
- Song, Haokun
- Lin, Rui
- Li, Yajie
- Lei, Qing
- Zhao, Yongli
- Wosinska, Lena
- Zhang, Jie
- Venue
- Optics Letters
- Publisher
- Optica Publishing Group
- Published
- 2023-06-07
- Volume
- 48
- Issue
- 12
- Pages
- 3183
- ISSN
- 0146-9592 (ISSN-L)
- 1539-4794
- OpenAlex
- W4367607846
- Cited by
- 21
- References
- 13
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 8 · 2023: 2
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Optics Letters (publishedVersion)
- PubMed (publishedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61831003, 62021005)
- VINNOVA (CELTIC-NEXT project AI-NET PROTECT)
- Jiangsu Engineering Research Center of Communication and Network Technology (Soochow University under Grant SDGC2117)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Network Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Photonic Communication Systems
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Keywords
- eavesdropping detection, time-domain signal features, long short-term memory networks, feature extraction, single-mode fiber transmission
- MeSH terms
- Machine Learning, Neural Networks, Computer
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