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International Conference on Transparent Optical Networks

Anomaly Classification in CV-QKD over DWDM: Differentiating System Faults from Quantum Attacks

Masab Iqbal, Michela Svaluto Moreolo, Laia Nadal

Published Jul 6, 2025• 1 citation
Publisher

Abstract

As continuous-variable quantum key distribution (CV-QKD) systems move toward integration with dense wavelength division multiplexing (DWDM) networks, distinguishing between external faults and malicious attacks becomes crucial for maintaining secure and high-availability key generation. While most existing studies focus on either attack mitigation or fault resilience independently, little attention has been paid to their joint classification. In this work, we present a machine learning-based approach to differentiate between system faults and quantum-level attacks in CV-QKD systems operating under DWDM coexistence. We emulate two practical filter-related faults—shifted filter frequency (SFF) and narrow filter bandwidth (NFB)—and a power-splitting channel tampering attack. Using Bob’s quadrature data (pB, qB) collected from a CV-QKD simulation test setup, we evaluate five classifiers: Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). We assess performance across various input sample sizes, measuring both classification accuracy and average prediction time. Our results show that while GNB offers the fastest predictions, it suffers from low accuracy. In contrast, DNN achieves near-perfect classification accuracy but at higher computational cost. RF presents a favorable trade-off, offering high accuracy with low latency. This work enables intelligent diagnosis of faults and attacks in telecom-integrated QKD systems, improving the uptime without compromising security.

DOI
10.1109/icton67126.2025.11125358

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As continuous-variable quantum key distribution (CV-QKD) systems move toward integration with dense wavelength division multiplexing (DWDM) networks, distinguishing between external faults and malicious attacks becomes crucial for maintaining secure and high-availability key generation. While most existing studies focus on either attack mitigation or fault resilience independently, little attention has been paid to their joint classification. In this work, we present a machine learning-based approach to differentiate between system faults and quantum-level attacks in CV-QKD systems operating under DWDM coexistence. We emulate two practical filter-related faults—shifted filter frequency (SFF) and narrow filter bandwidth (NFB)—and a power-splitting channel tampering attack. Using Bob’s quadrature data (pB, qB) collected from a CV-QKD simulation test setup, we evaluate five classifiers: Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). We assess performance across various input sample sizes, measuring both classification accuracy and average prediction time. Our results show that while GNB offers the fastest predictions, it suffers from low accuracy. In contrast, DNN achieves near-perfect classification accuracy but at higher computational cost. RF presents a favorable trade-off, offering high accuracy with low latency. This work enables intelligent diagnosis of faults and attacks in telecom-integrated QKD systems, improving the uptime without compromising security.
Misdiagnosing faults as attacks significantly reduces the key-generation uptime of CV-QKD systems. To address this, we characterize attacks and faults in CV-QKD systems and propose an approach for anomaly detection and its classification as faults and attacks in CV-QKD copropagating within dense wavelength division multiplexing (DWDM) systems. The proposed method combines analytical techniques for anomaly detection with a deep neural network (DNN) for accurate classification. This approach minimizes unnecessary system shutdowns and can be seamlessly integrated into SDN-enabled CV-QKD systems. Results demonstrate that traditional classifiers, such as Gaussian Naive Bayes (GNB), are insufficient for this task. Instead, the use of a DNN, despite its fourfold increase in prediction time, ensures significantly higher classification accuracy, thereby enhancing system reliability and operational efficiency.
Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying new attack vectors and countermeasures for CV-QKD implementations is important for the continued robustness of CV-QKD. In particular, as CV-QKD relies on a public quantum channel, vulnerability to communication disruption persists from potential adversaries employing denial-of-service (DoS) attacks. Inspired by DoS attacks, this paper introduces a threat in CV-QKD called the channel amplification (CA) attack, wherein Eve manipulates the communication channel through amplification. We specifically model this attack in a CV-QKD optical fiber setup. To counter this threat, we propose a detection and mitigation strategy. Detection involves a machine learning (ML) model based on a decision tree classifier, classifying various channel tampering attacks, including CA and DoS attacks. For mitigation, Bob, postselects quadrature data by classifying the attack type and frequency. Our ML model exhibits high accuracy in distinguishing and categorizing these attacks. The CA attack's impact on the secret key rate (SKR) is explored concerning Eve's location and the relative intensity noise of the local oscillator (LO). The proposed mitigation strategy improves the attacked SKR for CA attacks and, in some cases, for hybrid CA-DoS attacks. Our study marks an application of both ML classification and postselection in this context. These findings are important for enhancing the robustness of CV-QKD systems against emerging threats on the channel. Published by the American Physical Society 2024
Continuous-variable quantum key distribution (CV-QKD) with discrete modulation has received widespread attentions because of its experimental simplicity, lower-cost implementation and ease to multiplex with classical optical communication. Recently, some inspiring numerical methods have been applied to analyse the security of discrete-modulated CV-QKD against collective attacks, which promises to obtain considerable key rate over one hundred kilometers of fiber distance. However, numerical methods require up to ten minutes to calculate a secure key rate one time using a high-performance personal computer, which means that extracting the real-time secure key rate is impossible for discrete-modulated CV-QKD system. Here, we present a neural network model to quickly predict the secure key rate of homodyne detection discrete-modulated CV-QKD with good accuracy based on experimental parameters and experimental results. With the excess noise of about 0.010.010.01, the speed of our method is improved by about seven orders of magnitude compared to that of the conventional numerical method. Our method can be extended to quickly solve complex security key rate calculation of a variety of other unstructured quantum key distribution protocols.
Deep neural networks (DNNs) have been employed in continuous-variable quantum key distribution (CV-QKD) systems as attacking detection portions of defense countermeasures. However, the vulnerability of DNNs leaves security loopholes for hacking attacks, for example, adversarial attacks. In this paper, we propose to implement the one-pixel attack in CV-QKD attack detection networks and accomplish the misclassification on a minimum perturbation. This approach is based on the differential evolution, which makes our attack algorithm fool multiple DNNs with the minimal inner information of target networks. The simulation and experimental results show that, in four different CV-QKD detection networks, 52.8%, 26.4%, 21.2%, and 23.8% of the input data can be perturbed to another class by modifying just one feature, the same as one pixel for an image. We carry out this success rate in the context of the original accuracy reaching up to nearly 99% on average. Further, by enlarging the number of perturbed features, the success rate can be raised to a satisfactory higher level of about 80%. According to our experimental results, most of the CV-QKD detection networks can be deceived by launching one-pixel attacks.
Discretely modulated continuous-variable quantum key distribution (DM CV-QKD) protocols, while promising for extended transmission distances, face significant security threats in practice due to discrepancies between theoretical and actual system models. A critical vulnerability lies in the differential detector, which can be exploited by an eavesdropper to launch saturation attacks. In this paper, we conduct a comprehensive analysis of saturation attacks specifically targeting DM CV-QKD systems and propose a novel machine learning-based defense scheme utilizing the K-Nearest Neighbors (KNN) algorithm for attack detection. The simulation results confirmed that saturation attacks can undermine the actual security of the DM CVQKD system. The defense scheme proposed in this paper can accurately identify the probability of saturation attacks at 96.5%, and has been proven to be effective in defending against saturation attacks.
Continuous-variable quantum key distribution (CV-QKD) is a quantum communication technology that offers an unconditional security guarantee. However, the practical deployment of CV-QKD systems remains vulnerable to various quantum attacks. In this paper, we propose a quantum machine learning (QML)-based attack detection framework (QML-ADF) that safeguards the security of high-rate CV-QKD systems. In particular, two alternative QML models -- quantum support vector machines (QSVM) and quantum neural networks (QNN) -- are developed to perform noise-resistant and feature-aware attack detection before conventional data postprocessing. Leveraging feature-rich quantum data from Gaussian modulation and homodyne detection, the QML-ADF effectively detects quantum attacks, including both known and unknown types defined by these distinctive features. The results indicate that all twelve distinct QML variants for both QSVM and QNN exhibit remarkable performance in detecting both known and previously undiscovered quantum attacks, with the best-performing QSVM variant outperforming the top QNN counterpart. Furthermore, we systematically evaluate the performance of the QML-ADF under various physically interpretable noise backends, demonstrating its strong robustness and superior detection performance. We anticipate that the QML-ADF will not only enable robust detection of quantum attacks under realistic deployment conditions but also strengthen the practical security of quantum communication systems.