Detecting practical quantum attacks for continuous-variable quantum key distribution using density-based spatial clustering of applications with noise

Continuous-variable quantum key distribution (CVQKD) has been proven to be secure theoretically. However, the practical CVQKD system may still be subject to various quantum attacks due to the imperfections of devices. In this paper, we suggest a general machine learning-based defense strategy against practical quantum attacks by taking advantage of density-based spatial clustering of applications with noise (DBSCAN), which we called DBSCAN-based attack detection scheme (DADS). Specifically, we first construct a set of features that can well reflect the behaviors of different attacks, then DBSCAN is applied to obtain several clusters. This clustering result can explicitly indicate whether the CVQKD system is being eavesdropped or not. Simulation experiments show that the proposed DADS cannot only detect most of known attacks, but also has ability to identify various unknown attacks, thereby improving practical security of the CVQKD system. We also show that the overestimated secret key rate caused by ignoring practical quantum attacks can be amended by DADS so that a reasonable tighter secure bound of the practical CVQKD system can be obtained.

Automated summary

In this paper, a machine learning-based defense strategy called DADS is proposed to detect practical quantum attacks in the CVQKD system. By using the DBSCAN clustering method, the proposed scheme can effectively identify known and unknown attacks, improving the practical security of the system. The simulation results show that DADS can also provide a tighter secure bound for the CVQKD system by correcting the overestimated secret key rate caused by ignoring practical quantum attacks.