Journal
PHYSICAL REVIEW A
Volume 105, Issue 4, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.105.042411
Keywords
-
Categories
Funding
- National Natural Science Foundation of China (NSFC) [11904410, 61972418, 61977062, 61872390, 61871407, 61801522]
- Natural Science Foundation of Hunan Province, China [2019JJ40352]
- National Research Foundation, Singapore [NRF2021-QEP2-04-P01]
Ask authors/readers for more resources
This paper presents a semi-supervised deep learning method to detect known attacks or potential threats in quantum key distribution systems. Experimental results show that this method can overcome existing limitations and improve practical security.
Removing quantum hacking attacks is a highly challenging task for quantum key distribution (QKD) since one needs to correctly detect the types of attacks. There is a big gap between the idealized theoretical model and the practical physical system, which results in various hacking strategies exploited from security loopholes. To bridge this gap, previous continuous-variable (CV) QKD systems adopted a universal statistical model to represent different dimensional characters of the physical layer. The challenge is to discriminate different attacks from the exposed key physical details. Here we present a semisupervised deep-learning method to detect the known attacks or potential threats that are hard to be captured in previous CVQKD systems. The proof-of-principle experiment result shows that it could break the present limitations that different attacks are relatively independent, and key features computed from previous threshold-based methods are not correlated with each other. We anticipate that our proposal will give insight into how quantum hacking attacks can be detected under the real system modeling constraints, and will enable the present quantum communication systems to reach a higher level of practical security without adding new assumptions.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available