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Optical Fiber Technology

Experimental study of machine-learning-based detection and location of eavesdropping in end-to-end optical fiber communications

Haokun Song, Yajie Li, Mingzhe Liu, Kai Wang, Junjia Li, Mingrui Zhang, Yongli Zhao, Jie Zhang

Published Nov 27, 2021• 34 citations
Publisher
DOI
10.1016/j.yofte.2021.102669

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Fiber eavesdropping severely endangers the confidentiality of data transmitted in optical networks. Therefore, it is necessary to explore how to detect and locate fiber eavesdropping in an effective approach. To leverage the advantages of the state of polarization (SOP) in detecting various abnormal events while addressing its challenges in acquiring the SOP of different fiber links, we propose a multi-channel joint SOP estimation scheme to estimate the SOP of different fiber spans. Based on the proposed scheme, we provide a comprehensive solution for fiber eavesdropping location and detection in optical communication systems. In this solution, the estimated SOP and optical performance monitoring (OPM) data are utilized for rapid fiber eavesdropping detection and coarse location at the span level. The effectiveness of the solution is validated by experiments. In the aspect of detection, we achieve the detection of the start or end of fiber eavesdropping, the overlapping of fiber eavesdropping and abnormal events, and other abnormal events. The overall accuracy is 99.77%. In the aspect of location, we can locate the fiber span that has been eavesdropped.

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As the backbone of global communication infrastructure, optical fibers carry vast amounts of sensitive data, making them prime targets for eavesdropping attacks that can compromise data confidentiality and national security. However, optical fibers are highly vulnerable to eavesdropping attacks-such as fiber bending or evanescent coupling-which can eavesdrop on data without interrupting the service. Accurate detection of eavesdropping remains a critical challenge, particularly under complex environmental disturbances. Environmental factors such as fluctuation and disturbances can induce signal distortions and polarization variations that mask or mimic eavesdropping characteristics, thereby posing a significant challenge to the training and generalization of machine learning-based classifiers. In this Letter, we propose a sensing framework based on a hybrid gated recurrent unit-convolutional neural network model that jointly analyzes polarization parameters and error vector magnitude. The proposed method enables accurate and robust eavesdropping detection and temporal localization under complex physical environments. Experiments conducted across seven practical scenarios demonstrate that the proposed scheme achieves a detection accuracy of 97.2%. Furthermore, we developed real-time and offline optical fiber monitoring platforms integrating mainstream detection techniques to facilitate comprehensive evaluation and deployment.

Optics Letters•Mar 12, 2026•0 citations
Fiber eavesdropping severely endangers the confidentiality of data transmitted in optical networks. Therefore, it is necessary to explore how to detect and locate fiber eavesdropping in an effective approach. To leverage the advantages of the state of polarization (SOP) in detecting various abnormal events while addressing its challenges in acquiring the SOP of different fiber links, we propose a multi-channel joint SOP estimation scheme to estimate the SOP of different fiber spans. Based on the proposed scheme, we provide a comprehensive solution for fiber eavesdropping location and detection in optical communication systems. In this solution, the estimated SOP and optical performance monitoring (OPM) data are utilized for rapid fiber eavesdropping detection and coarse location at the span level. The effectiveness of the solution is validated by experiments. In the aspect of detection, we achieve the detection of the start or end of fiber eavesdropping, the overlapping of fiber eavesdropping and abnormal events, and other abnormal events. The overall accuracy is 99.77%. In the aspect of location, we can locate the fiber span that has been eavesdropped.
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.
As the backbone of global communication infrastructure, optical fibers carry vast amounts of sensitive data, making them prime targets for eavesdropping attacks that can compromise data confidentiality and national security. However, optical fibers are highly vulnerable to eavesdropping attacks-such as fiber bending or evanescent coupling-which can eavesdrop on data without interrupting the service. Accurate detection of eavesdropping remains a critical challenge, particularly under complex environmental disturbances. Environmental factors such as fluctuation and disturbances can induce signal distortions and polarization variations that mask or mimic eavesdropping characteristics, thereby posing a significant challenge to the training and generalization of machine learning-based classifiers. In this Letter, we propose a sensing framework based on a hybrid gated recurrent unit-convolutional neural network model that jointly analyzes polarization parameters and error vector magnitude. The proposed method enables accurate and robust eavesdropping detection and temporal localization under complex physical environments. Experiments conducted across seven practical scenarios demonstrate that the proposed scheme achieves a detection accuracy of 97.2%. Furthermore, we developed real-time and offline optical fiber monitoring platforms integrating mainstream detection techniques to facilitate comprehensive evaluation and deployment.