To protect the confidentiality of optical networks, we analyze the influence of fiber eavesdropping on the state of polarization and propose a multi-channel joint polarization estimation scheme to detect and locate fiber eavesdropping. By modeling the fiber eavesdropping as the rotation of the state of polarization through experiment data, the scheme’s effectiveness is further verified in the simulation of multi-span WDM system.
To protect the confidentiality of optical networks, we analyze the influence of fiber eavesdropping on the state of polarization and propose a multi-channel joint polarization estimation scheme to detect and locate fiber eavesdropping. By modeling the fiber eavesdropping as the rotation of the state of polarization through experiment data, the scheme’s effectiveness is further verified in the simulation of multi-span WDM system.
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.
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.
Ensuring the secure and reliable operation of optical networks is crucial for various societal functions. However, optical network infrastructures are susceptible to unauthorized interception, posing a significant security risk at the physical layer. This necessitates the development of effective detection and localization methods of eavesdropping events. To address this challenge, we present a clustering-based method and a comprehensive eavesdropping diagnosis framework tailored for wavelength division multiplexing (WDM) systems. The framework is designed to handle diverse eavesdropping scenarios, including dynamic detection, classification, and localization of eavesdropping events. To mitigate the data dependency issue while detecting and localizing eavesdropping events, we propose a clustering algorithm utilizing basic optical performance monitoring (OPM) data, thus eliminating the need for sophisticated measurement equipment. A coarse localization requires only the OPM data from the receiver, while a finer localization requires the power monitoring data at all nodes as the input. The feasibility of the proposed scheme is validated using simulation-generated data, in which single and multiple eavesdropping can be detected and localized with a 100% label matching rate. Single-point eavesdropping detection and localization are experimentally validated with data collected from a fiber transmission system comprising three spans of 40 km each. Coarse localization with a 99.79% label matching rate and fine localization with 100% accuracy is achieved. As expected, experimental data shows a less concentrated distribution than the simulated data, which leads to inferior clustering results.
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.