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IEEE Access

Saturation Attacks on DM CV-QKD: Threat Analysis and Machine Learning-Based Defense

Ye Li

Published Jan 1, 2026• 0 citations
Publisher

Abstract

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.

DOI
10.1109/access.2026.3663588

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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.
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-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.
The discrete-modulated continuous-variable quantum key distribution (DM-CV-QKD) could break the distance limitation of Gaussian-modulated CV-QKD. In practice, the high-performance error correction code plays an important role in DM-CV-QKD and affects the secure transmission distance. However, DM-CV-QKD usually works under low signal-to-noise ratio (SNR) and the design of high-performance error correction code under this condition is difficult, so that it would impose a limitation on further improvement of the secure distance. We propose a DM-CV-QKD with the machine-learning-based detector to further improve the secure distance. The numerical result shows that the proposed scheme could validly improve the system performance. Viewed from another perspective, the proposed scheme could be employed to overcome various impairments induced by the channel and thereby lower the demand of error correction codes on the SNR threshold of the quantum channel without compromising the system performance. The proposed scheme opens the door to applying machine leaning to directly process the raw secret key and improve the performance for CV-QKD systems.
Continuous-variable quantum key distribution (CVQKD) over an atmosphere channel provides the possibility of global coverage, which is threatened by the imperfection of actual devices. Exploiting imperfections of practical devices, an eavesdropper steals the information between legal participants by performing the potential saturation-induced attacks on the homodyne detector. In this paper, we demonstrate the feasibility of the potential saturation attack in discretely modulated (DM) CVQKD over atmospheric turbulence. Subsequently, a countermeasure is suggested by using an adjustable optical filter (AOF) to attenuate the estimated parameter with the measured value in the finite linearity domain, where the attenuated data can be compensated. Numerical simulations illustrate the effects of the saturation attack on the performance of the AOF-based CVQKD system. We find that legal participants evaluate the information that has been eavesdropped because the secret key rate is made negative.
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.
Abstract Continuous variable quantum key distribution (CVQKD) can achieve the secure communication between two distant legitimate users. The microwave CVQKD exhibits stronger robustness than the optical CVQKD in transmitting quantum signals and accordingly has broad application prospects in quantum networks. Although the microwave CVQKD does not rely on the traditional optical fibers to transmit quantum signals, its practical security is still vulnerable to saturation attacks. In view of this security vulnerability, we generalize saturation attacks to microwave CVQKD and clarify the impact of saturation attacks on the parameter estimation of microwave systems. Two saturation attack schemes are further compared, one based on displacement control alone and the other on joint displacement and gain control. To mitigate the negative impacts caused by this attack, we propose a countermeasure based on the quantum k nearest neighbor algorithm, which can detect saturation attacks by measuring unknown quantum states. In addition, the influence of microwave antennas on the security of the communication system is assessed, confirming that antenna gain plays a key role in determining both the secret key rate and the maximum transmission distance.