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Journal of the Optical Society of America B

Attack detection scheme for continuous variable quantum key distribution based on a graph neural network

Di Jin, Wenqi Jiang, Ying Guo, Junkai Hu, Duan Huang

Published May 3, 2024• 5 citations
Publisher

Abstract

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.

DOI
10.1364/josab.524779

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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.
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.
Hacking vulnerabilities in continuous-variable quantum key distribution (CV-QKD) systems are investigated, focusing on practical imperfections in homodyne detection. Specifically, these flaws can be exploited in detector-blinding and detector-saturation attacks, where an eavesdropper can undermine secure communication, especially in CV-QKD. Further, a neural network-based detection method is implemented as a potential countermeasure to classify homodyne detector output voltage data, aiding in identifying such potential hacking scenarios. We obtain an overall accuracy of over 90%90 \%90% in identifying the attack scenario utilizing a MATLAB neural network model.
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.
The security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the general detection model emerges to improve the universality of detection; however, current universal detection methods only consider the independent existence of attacks but ignore the possible coexistence of multiple attacks in reality. Here, we propose two multi-attack neural network detection models to handle the coexistence of multiple attacks. The models adopt two methods in multi-label learning: binary relevance (BR) and label power (LP) to deal with the coexistence of multiple attacks and can identify attacks in real-time by autonomously learning the features of known attacks in a deep neural network. Further, we improve the model to detect unknown attacks simultaneously. The experimental results show that the proposed scheme can achieve high-precision detection for most known and unknown attacks without reducing the key rate and maximum transmission distance.
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.
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.