Denial-of-Service Attack Defense Strategy for Continuous Variable Quantum Key Distribution via Deep Learning

Abstract

In the practical Continuous Variable Quantum Key Distribution (CVQKD) system, there is a large gap between the ideal theoretical model and the actual physical system. There are still some inevitable flaws, which give quantum hackers the opportunity to manipulate the channel in complex communication environments and launch Denial of Service attacks on the quantum channel. Therefore, a DoS attack-aware defense scheme for the CVQKD system based on convolutional neural networks (CNN) is proposed. The simulation results show that the proposed model can effectively detect DoS attacks launched by quantum hackers in CVQKD system in a complex communication environment, and the model has strong robustness due to the addition of the attention mechanism module. In addition, multiple sets of comparative experiments show that compared with the existing artificial neural network model, the CNN-based model has higher accuracy and stability.