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Journal of Optical Communications and Networking

Cluster-based unsupervised method for eavesdropping detection and localization in WDM systems

Song Hao-kun, Rui Lin, Lena Wosinska, Paolo Monti A, Mingrui Zhang, Yuyuan Liang, Yajie Li, Jie Zhang

Published Aug 8, 2024• 12 citations
Publisher

Abstract

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.

DOI
10.1364/jocn.531696

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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.
Leveraging our initial work on detecting eavesdropping events in WDM optical systems [1], we propose a mechanism that utilizes bisecting k-means on dynamic optical performance monitoring (OPM) data to initialize the detection. We develop a method to detect and localize single and multiple eavesdropping events in WDM optical systems. Very small losses caused by eavesdropping can be detected using OPM data collected at the receiver, while the in-line OPM data enables localizing single and multiple eavesdropping events.
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 telecommunication networks and in computer grid networks, a significant ongoing research is in a technology known as wavelength division multiplexing (WDM), which is capable to transport more than 1 Tbps aggregate traffic over a single fiber. However, when massive amounts of information are transported, as in optical networks, there are three issues that stand, cost-efficient ultra-high bandwidth delivery, security of information and data, and security of the network and of the transporting link layer. In optical networks, each fiber link consists of segments, each several kilometers long. Since a single WDM channel carries traffic, there are opportunities for sophisticated eavesdroppers to tap the fiber, copy a channel, isolate a specific payload and attempt to decrypt it if the encryption key is known or it is breakable with a fast computer. Therefore, end-to-end data security should not only rely on the client's encryption method but also on securing the transmission method on the optical link. In this paper, we present a WDM link security methodology that encrypts multiple channels and with multiple keys to render channel monitoring by eavesdroppers virtually impossible. Although our paper presents the applicability of the method on a point-to-point topology, it is applicable to a multilink and edge-to-edge network path.
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.
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.