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Chinese Physics B

General multi-attack detection for continuous-variable quantum key distribution with local local oscillator

Zhuo 茁 Kang 康, Wei-Qi 维琪 Liu 刘, Jin 锦 Qi 齐, Chen 晨 He 贺

Published Feb 22, 2024• 2 citations
Publisher

Abstract

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.

DOI
10.1088/1674-1056/ad2bf2

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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.
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.
Machine learning is being applied to continuous-variable quantum key distribution (CVQKD) systems as defense countermeasures for attack classification. However, recent studies have demonstrated that most of these detection networks are not immune to adversarial attacks. In this paper, we propose to implement typical adversarial attack strategies against the CVQKD system and introduce a generalized defense scheme. Adversarial attacks essentially generate data points located near decision boundaries that are linearized based on iterations of the classifier to lead to misclassification. Using the DeepFool attack as an example, we test it on four different CVQKD detection networks and demonstrate that an adversarial attack can fool most CVQKD detection networks. To solve this problem, we propose an improved adversarial perturbation elimination with a generative adversarial network (APE-GAN) scheme to generate samples with similar distribution to the original samples to defend against adversarial attacks. The results show that the proposed scheme can effectively defend against adversarial attacks including DeepFool and other adversarial attacks and significantly improve the security of communication systems.
The estimation of phase noise of continuous-variable quantum key distribution protocol with a local local oscillator (LLO CVQKD), as a major process in quantifying the secret key rate, is closely relevant to the intensity of the phase reference. However, the transmission of the phase reference through the insecure quantum channel is prone to be exploited by the eavesdropper (Eve) to mount attacks. Here, we introduce a polarization attack scheme against the phase reference. Presently, in a practical LLO CVQKD system, only part of the phase reference pulses are measured to compensate for the polarization drift of the quantum signal pulses in a compensation cycle due to the limited polarization measurement rate, while the other part of the phase reference pulses are not measured. We show that Eve can control the phase noise by manipulating the polarization direction of the unmeasured phase reference to hide her attack on the quantum signal. Simulations show that Eve can obtain partial or total key rates information shared between Alice and Bob as the transmission distance increases. Improving the polarization measurement rate to 100% or monitoring the phase reference intensity in real-time is of great importance to protect the LLO CVQKD from polarization attack.
In practical continuous-variable quantum key distribution system using local local oscillator (LLO CV-QKD), the phase noise related to coherent detection and phase-reference pulse intensity can be locally calibrated at the receiver side. Therefore, it can be regarded as trusted noise in order to obtain a higher secret key rate and transmission distance. However, security loopholes will be opened for the eavesdropper to launch attacks without appropriate countermeasures. To this end, we propose two practical phase-reference pulse intensity attack strategies, where Eve can reduce the trusted phase noise by manipulating the intensity of the phase-reference pulse, thereby hiding her attack on the signal pulse. The performance of the LLO CV-QKD system under these attacks has been analyzed. We show that precisely monitoring the intensity of the phase-reference pulse in real time is an essential countermeasure to prevent the proposed attacks. Moreover, an intensity-monitoring scheme for the phase-reference pulse is proposed to strengthen the security of the practical LLO CV-QKD system and make the trusted phase noise model more robust.
The security of continuous-variable quantum key distribution (CVQKD) systems faces severe challenges from quantum hacking attacks in practical deployments. This paper proposes a novel hybrid quantum-classical neural network (HQCNN) architecture for the detection of quantum hacking attacks. This architecture employs a convolutional neural network (CNN) to extract features from raw pulse signals at the receiver and to reduce spatial dimensionality. Subsequently, the extracted features are mapped into a high-dimensional Hilbert space via angle encoding, and a variational quantum circuit (VQC) is utilized as the core classifier for discrimination. In five-class classification experiments involving local oscillator intensity attacks (LOIA), calibration attacks, saturation attacks, hybrid attacks, and the no-attack state, the HQCNN achieves an overall accuracy of 93%, representing a 6% improvement over the classical residual network (ResNet). In addition, the proposed HQCNN architecture exhibits a significant advantage in parameter efficiency compared with classical deep neural networks. This study provides an efficient intelligent detection scheme for enhancing the practical security of 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.