Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution

Abstract

There exist various attack strategies in continuous-variable quantum key distribution (CVQKD) system in practice. Due to the powerful information processing ability of neural networks, they are applied to the detection and classification of attack strategies in CVQKD systems. However, neural networks are vulnerable to adversarial attacks, resulting in the CVQKD system using neural networks also having security risks. To solve this problem, we propose a defense scheme for the CVQKD system. We first perform low-rank dimensionality reduction on the CVQKD system data through regularized self-representation-locality preserving projects (RSR-LPP) to filter out some adversarial disturbances, and then perform sparse coding reconstruction through dictionary learning to add data details and filter residual adversarial disturbances. We test the proposed defense algorithm in the CVQKD system. The results indicate that our proposed scheme has a good monitoring and alarm effect on CVQKD adversarial disturbances and has a better effect than other compared defense algorithms.

A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution

“…While ML has shown potential to enhance several aspects of CV-QKD, its application needs to be carefully scrutinized with respect to the introduction of security vulnerabilities (e.g., [41,42]), where existing proofs may no longer hold. Finally, ML also has potential for QKD protocol selection (as was investigated in [65] for discrete-variable QKD) and could additionally be expanded to protocol optimization and design.…”

“…Research on ML-assisted quantum attacks [32,33], attack detection and prevention [27,[34][35][36][37][38][39][40], and methods for hacking ML-based attack prevention strategies [41,42] is not included in this survey. Huang, Liu, and Zhang [43] have previously reviewed the literature in this area.…”

“…Huang, Liu, and Zhang [43] have previously reviewed the literature in this area. We note that [33,[37][38][39][40][41][42] were published after [43] so were not included in their review. Further, this survey does not include works on ML for quantum communication in general, only addressing works specifically on the application of CV-QKD.…”

A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution

“…Not included in this survey, is research on ML-assisted quantum attacks [30,31], attack detection and prevention [25,[32][33][34][35][36][37][38], and methods for hacking ML-based attack prevention strategies [39,40]. Huang, Liu, and Zhang [41] have previously reviewed the literature in this area.…”

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

“…In this paper, to improve the practical security of LLO CVQKD, we propose a general defense strategy via deep learning that can be monitored in real-time without adding additional optics considering the development of machine learning opens a door for QKD. [27][28][29][30][31][32][33][34][35] We investigate the typical characteristics of pulses affected by attacks and the deviations in the changes in these characteristics after being subjected to different attacks. After analysis, several features are selected to construct feature vectors for training generative adversarial networks (GANs).…”