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Detecting Practical Attacks for Continuous-Variable Quantum Key Distribution Using Quantum k-Nearest Neighbor

Chan Liu, Junhao Li, Qin Liao

Published Aug 9, 2026• 0 citations
Publisher

Abstract

Continuous-variablequantum key distribution (CVQKD) enables information-theoretically secure communications between two legitimate users. However, practical CVQKD systems remain vulnerable to various attacks, while existing machine-learning-based detection schemes suffer from increasing computational complexity when processing large-scale monitoring data. In this paper, we propose a quantum k-nearest neighbor (QkNN)-based multiclass attack detection framework for CVQKD systems. Specifically, physical features extracted from Bob’s monitoring data are encoded into quantum states and subsequently classified using the QkNN algorithm to identify different attack behaviors. Furthermore, by incorporating the attack ratio, retained-data ratio, and attack detection performance into secret key rate analysis, an attack-aware secret key rate model is established to characterize the influence of practical attacks on key generation. Simulation results demonstrate that the proposed scheme achieves high attack classification accuracy while significantly reducing computational complexity. Moreover, the proposed framework establishes a direct connection between attack detection and secret key generation, enabling a more realistic security evaluation for practical CVQKD systems.

DOI
10.3390/info17080763

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Continuous-variablequantum key distribution (CVQKD) enables information-theoretically secure communications between two legitimate users. However, practical CVQKD systems remain vulnerable to various attacks, while existing machine-learning-based detection schemes suffer from increasing computational complexity when processing large-scale monitoring data. In this paper, we propose a quantum k-nearest neighbor (QkNN)-based multiclass attack detection framework for CVQKD systems. Specifically, physical features extracted from Bob’s monitoring data are encoded into quantum states and subsequently classified using the QkNN algorithm to identify different attack behaviors. Furthermore, by incorporating the attack ratio, retained-data ratio, and attack detection performance into secret key rate analysis, an attack-aware secret key rate model is established to characterize the influence of practical attacks on key generation. Simulation results demonstrate that the proposed scheme achieves high attack classification accuracy while significantly reducing computational complexity. Moreover, the proposed framework establishes a direct connection between attack detection and secret key generation, enabling a more realistic security evaluation for practical CVQKD systems.
Abstract The practical security of a continuous-variable quantum key distribution (CVQKD) system is compromised by various attack strategies. The existing countermeasures against these attacks are to exploit different real-time monitoring modules to prevent different types of attacks, which significantly depend on the accuracy of the estimated excess noise and lack a universal defense method. In this paper, we propose a defense strategy for CVQKD systems to address these disadvantages and resist most of the known attack types. We investigate several features of the pulses that would be affected by different types of attacks, derive a feature vector based on these features as the input of an artificial neural network (ANN) model, and show the training and testing process of the ANN model for attack detection and classification. Simulation results show that the proposed scheme can effectively detect most of the known attacks at the cost of reducing a small part of secret keys and transmission distance. It establishes a universal attack detection model by simply monitoring several features of the pulses without knowing the exact type of attack in advance.
Continuous-variable quantum key distribution (CVQKD) is a mature technology that can theoretically provide an unconditional security guarantee. However, a practical CVQKD system may be vulnerable to various quantum hacking attacks due to imperfect devices and insufficient assumptions. In this paper, we propose a universal defense strategy called a machine-learning-based attack detection scheme (MADS). Leveraging the combined advantages of density-based spatial clustering of applications with noise (DBSCAN) and multiclass support vector machines (MCSVMs), MADS demonstrates remarkable effectiveness in detecting quantum hacking attacks. Specifically, we first establish a set of attack-related features to extract feature vectors. These vectors are then utilized as input data for DBSCAN to identify and remove any noise or outliers. Finally, the trained MCSVMs are employed to classify and predict the processed data. The predicted results can immediately determine whether or not to generate a final secret key. Simulation results show that the proposed MADS can efficiently detect most quantum hacking attacks and revise the overestimated secret key rates caused by a CVQKD system without any defense strategy to obtain a tighter security bound.
Continuous-variable quantum key distribution (CVQKD) has been proven to be secure theoretically. However, the practical CVQKD system may still be subject to various quantum attacks due to the imperfections of devices. In this paper, we suggest a general machine learning-based defense strategy against practical quantum attacks by taking advantage of density-based spatial clustering of applications with noise (DBSCAN), which we called DBSCAN-based attack detection scheme (DADS). Specifically, we first construct a set of features that can well reflect the behaviors of different attacks, then DBSCAN is applied to obtain several clusters. This clustering result can explicitly indicate whether the CVQKD system is being eavesdropped or not. Simulation experiments show that the proposed DADS cannot only detect most of known attacks, but also has ability to identify various unknown attacks, thereby improving practical security of the CVQKD system. We also show that the overestimated secret key rate caused by ignoring practical quantum attacks can be amended by DADS so that a reasonable tighter secure bound of the practical CVQKD system can be obtained.
Continuous-variable quantum key distribution with a local local oscillator (LLO CVQKD) has been extensively researched due to its simplicity and security. For practical security of an LLO CVQKD system, there are two main attack modes referred to as reference pulse attack and polarization attack presently. However, there is currently no general defense strategy against such attacks, and the security of the system needs further investigation. Here, we employ a deep learning framework called generative adversarial networks (GANs) to detect both attacks. We first analyze the data in different cases, derive a feature vector as input to a GAN model, and then show the training and testing process of the GAN model for attack classification. The proposed model has two parts, a discriminator and a generator, both of which employ a convolutional neural network (CNN) to improve accuracy. Simulation results show that the proposed scheme can detect and classify attacks without reducing the secret key rate and the maximum transmission distance. It only establishes a detection model by monitoring features of the pulse without adding additional devices.
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.
The practical security of a continuous-variable quantum key distribution (CV-QKD) system is vulnerable to various attack strategies due to the significant difference between the idealized theoretical model and the practical physical system. The existing countermeasures against these attacks involve exploiting different real-time monitoring modules, which presents a challenge in effectively classifying attacks. We investigate a graph neural network (GNN)-based attack detection scheme for CV-QKD, which models data as a graph structure using three different methods for various conditions. Particularly, one of the proposed methods requires no additional devices and can detect attacks with over 99% accuracy. The algorithm can be expanded to different scenarios without additional training and can achieve a detection efficiency of more than 95%. Furthermore, our proposed scheme incorporates anomaly detection algorithms into the detection module, enabling 85% effective detection of partially unknown attacks with minimal security data.