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Asia Communications and Photonics Conference

Experimental Demonstration of Optical Fiber Eavesdropping Detection Based on Deep Learning

Mingzhe Liu, Yajie Li, Haokun Song, Zhiwei Tu, Yongli Zhao, Jie Zhang

Published Nov 2, 2019• 5 citations
OpenAlex

Abstract

This paper proposes an effective eavesdropping detection scheme based on deep learning. The feasibility is verified in the experiment with 300km optical transmission. Results show that detection accuracy of eavesdropping can be achieved over 95%.

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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.
Optical fiber communication is the only technical means of supporting high transmission rates. Since optical fibers are insulators and can resist electromagnetic interference, they were once considered to be highly secure. However, due to the endless means of eavesdropping, optical fibers are vulnerable to eavesdropping, which poses a serious threat to the data security of optical fiber communication infrastructures. Optical fiber eavesdropping detection is one of the strategies to effectively safe transmission of data. Due to insufficient mining of eavesdropping information or limited by the performance of the sensor, it cannot be detected with high accuracy. To deal with this problem, we propose a convolutional neural networks-based scheme for de-tecting fiber bending eavesdropping in 16QAM coherent optical communication systems. Numerical results show 99.1%, 99%, and 98.5% detection accuracy at bending radii of 10.8 mm, 12.1 mm, and 15 mm, respectively.
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.
Optical fiber communication networks are the backbone of information transmission, carrying over 90% of the world's data. However, optical fibers are susceptible to fiber-bending eavesdropping attacks, which can result in massive data leaks. The detection of fiber-bending eavesdropping attacks has become one of the effective strategies to safeguard data security. In this paper, we propose a detection scheme based on a convolutional neural network (CNN) for detecting fiber-bending eavesdropping attacks in dual-polarization coherent optical communication systems. To validate the feasibility and effectiveness of the proposed scheme, we first establish the relationship between the fiber-bending radius and the bending power loss coefficient. Based on this relationship, a 100 km, 25 GBaud dual-polarization 16 quadrature amplitude modulation (16QAM) coherent optical communication system is constructed. This system collects both normal polarization data and bending eavesdropping polarization data. Subsequently, the performance of the CNN algorithm in detecting fiber-bending eavesdropping attacks is evaluated. The results show that under bending radii of 10.8 mm, 12.1 mm, 15 mm, and mixed bending radii, the eavesdropping detection accuracy rates of the CNN reach 99.8%, 99.67%, 99.22%, and 96.15%, respectively.