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arXiv:2309.14541 · Asia Communications and Photonics Conference

Cluster-Based Method for Eavesdropping Identification and Localization in Optical Links

Haokun Song, Rui Lin, Andrea Sgambelluri, Filippo Cugini, Yajie Li, Jie Zhang, Paolo Monti A

Published Nov 4, 2023• 2 citations
arXivHTMLPDF

Abstract

We propose a cluster-based method to detect and locate eavesdropping events in optical line systems characterized by small power losses. Our findings indicate that detecting such subtle losses from eavesdropping can be accomplished solely through optical performance monitoring (OPM) data collected at the receiver. On the other hand, the localization of such events can be effectively achieved by leveraging in-line OPM data.

DOI
10.1109/acp/poem59049.2023.10369850

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We propose a cluster-based method to detect and locate eavesdropping events in optical line systems characterized by small power losses. Our findings indicate that detecting such subtle losses from eavesdropping can be accomplished solely through optical performance monitoring (OPM) data collected at the receiver. On the other hand, the localization of such events can be effectively achieved by leveraging in-line OPM data.
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.
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.
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 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.
This paper focuses on the analysis of fiber optic line eavesdropping options based on cheap and easy-to-use equipment - for example, the commonly used fiber optic splitters with suitable optical power division ratios. The fiber optic splitter takes a small portion of the optical power sufficient for the eavesdropper to read the data and lets as much signal power as possible pass in the original direction. We attempted to detect the presence of fiber optic splitter-based eavesdropping points on the communication line by using common techniques designated for fiber optic quality measurement and fault detection. The results are summarised in this paper.
Optical fiber communication was widely believed that an optical network was a good way to prevent hacking through wiretaps which intercept network data. However, fiber optic cable is not particularly secure. Various tapping methods can be used to extract data from fiber optic networks with little or no risk of detection. One of the easiest ways of intruding into a fiber optic communication link is creating a bend on the fiber cable. Even small percentage of the transmitted light acquired with the bending method is enough for recovering the data by the attacker. Though there are some techniques and devices that can track the power and intensity inside the fiber cable, these methods cannot detect an attack exploiting small leakage. Therefore, it is critical to determine a range of radii of curvature for which tapping attacks are not detectable. Though there are some studies to quantify the power loss both theoretically and numerically, as of the authors' knowledge, there has not been any experimental validation of those studies. In this respect, an experimental optical setup and a technique relying on the ADC conversion capability of Arduino UNO to measure power loss resulting from different bending radii are proposed in this paper. A lower limit for radius of curvature for an attack to be undetected is obtained and the estimations in the literature for power loss resulting from fiber bending is validated.