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.
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 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.
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.
This paper proposes a physics-informed convolutional network (PICN) scheme to detect bending eavesdropping attacks in dual-polarization coherent optical communication systems. We present a theoretical model for optical signal transmission under bending eavesdropping, analyzing the impact of bending eavesdropping on fiber physical characteristics such as dispersion and nonlinear effect. These physical characteristics are embedded into a convolutional neural network (CNN) to construct PICN, which automatically captures subtle variations of the signal features under bending eavesdropping. To validate the effectiveness of the scheme, we first develop an eavesdropping experimental platform in an 80-km 168 Gbps dual-polarization quadrature phase shift keying (QPSK) coherent optical communication system. Polarization data are then collected under normal transmission, 10.8 mm and 15 mm bending radius. Finally, the detection performance of four classifiers including PICN, random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) are evaluated at single and mixed bending radii. Experimental results demonstrate that PICN achieves detection accuracies of 100%, 98.53%, and 99.02% under 10.8 mm, 15 mm, and mixed bending radii, respectively. Our work provides novel theoretical foundations and innovative perspectives for bending eavesdropping detection in optical fiber communication systems.
Fiber optic cables have long been considered a secure medium that prevents eavesdropping due to their complexity. However, there are attack methods that allow eavesdropping or denial of service. These include methods based on bending optical fibers. The danger resides in the possibility of being able to carry out an attack without having to interrupt the path. To facilitate such attacks, it is possible to use a clip-on coupler. This coupler is designed to allow eavesdropping on the link without interruption. In this study, we focus on evaluating the coupling efficiency of a simple but commercially available clip-on coupler. This work investigates the effectiveness of jamming into the optical link, i.e., replicating the jamming attack and measuring it by simulating real traffic conditions. To better understand the complexity of these attacks, we perform additional measurements to investigate the effect of clip-on coupling on changes in the polarization state. Through this research, we highlight the increasing vulnerability of optical fibers to eavesdropping attempts, especially those using macro-based methods. By evaluating the performance of commercially available clip-on coupling, we provide valuable insights into potential risks and mitigation strategies for securing modern optical communication networks.
This paper proposes a physics-informed neural network (PINN) scheme for localizing bending eavesdropping in coherent optical communication systems. First, we establish a signal transmission model under bending eavesdropping based on the Manakov equations to analyze its impact on physical information such as linear birefringence and nonlinearity. Subsequently, a PINN is developed by incorporating physical information such as linear birefringence and nonlinear effects into a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) architecture. To validate the effectiveness of the scheme, we construct an experimental platform for bending eavesdropping localization in an 80 km, 168 Gbps quadrature phase shift keying (QPSK) coherent optical communication system. Polarization data during eavesdropping are collected from nine positions with bending radii of 10.8 mm and 15 mm. The performance of three models, including CNN, CNN-BiLSTM and PINN is evaluated for bending eavesdropping localization under both bending radii. Experimental results demonstrate that the PINN achieves localization accuracies of 100% and 99.8% under bending radii of 10.8 mm and 15 mm, respectively. This work offers a novel theoretical framework and methodological approach for localizing bending eavesdropping in optical fiber communication systems.
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.