With increasing dependence on secure access to digital services and the ultra-high traffic volumes running on the optical fibre communication infrastructure, the protection of this infrastructure from eavesdropping is extremely important, especially in defense and military applications. The G.657 fibre is recommended to be deployed in in-building installations for its improved bending performance compared to the G.652 fibre. However, the easiness to be eavesdropped, which reflects the security level of those two types of fibres has not yet been investigated. In this paper, we study the eavesdropping of fibre from a system perspective and compare the bending property of G.652 and G.657 fibres. The measurement results show that G.657 can be bent sharper than G.652 without causing any additional power attenuation at the receiver. This indicates that the so-called bending-insensitive G.657 fibre can potentially be eavesdropped more easily than their G.652 counterparts. The paper also shows that the power level measurement at the receiver may not be sufficient for unambiguous eavesdrop detection.
With increasing dependence on secure access to digital services and the ultra-high traffic volumes running on the optical fibre communication infrastructure, the protection of this infrastructure from eavesdropping is extremely important, especially in defense and military applications. The G.657 fibre is recommended to be deployed in in-building installations for its improved bending performance compared to the G.652 fibre. However, the easiness to be eavesdropped, which reflects the security level of those two types of fibres has not yet been investigated. In this paper, we study the eavesdropping of fibre from a system perspective and compare the bending property of G.652 and G.657 fibres. The measurement results show that G.657 can be bent sharper than G.652 without causing any additional power attenuation at the receiver. This indicates that the so-called bending-insensitive G.657 fibre can potentially be eavesdropped more easily than their G.652 counterparts. The paper also shows that the power level measurement at the receiver may not be sufficient for unambiguous eavesdrop detection.
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.
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.
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.
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.