Detecting quantum attacks: a machine learning based defense strategy for practical continuous-variable quantum key distribution

Abstract

The practical security of a continuous-variable quantum key distribution (CVQKD) system is compromised by various attack strategies. The existing countermeasures against these attacks are to exploit different real-time monitoring modules to prevent different types of attacks, which significantly depend on the accuracy of the estimated excess noise and lack a universal defense method. In this paper, we propose a defense strategy for CVQKD systems to address these disadvantages and resist most of the known attack types. We investigate several features of the pulses that would be affected by different types of attacks, derive a feature vector based on these features as the input of an artificial neural network (ANN) model, and show the training and testing process of the ANN model for attack detection and classification. Simulation results show that the proposed scheme can effectively detect most of the known attacks at the cost of reducing a small part of secret keys and transmission distance. It establishes a universal attack detection model by simply monitoring several features of the pulses without knowing the exact type of attack in advance.

Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios

“…In [13], machine learning is used to detect different attack techniques that undermine the functional security of a continuous-variable quantum key distribution (CVQKD) framework. The authors of [13] suggest a security technique for CVQKD systems to the most recognized forms of attacks.…”

“…In [13], machine learning is used to detect different attack techniques that undermine the functional security of a continuous-variable quantum key distribution (CVQKD) framework. The authors of [13] suggest a security technique for CVQKD systems to the most recognized forms of attacks. They analyzed multiple pulse characteristics that would be influenced by various types of attacks, extracted a feature vector based on these characteristics as an artificial neural network (ANN) model input, and illustrated the ANN model's preparation and testing method for attack identification and classification.…”

Secure Continuous-Variable Quantum Key Distribution with Machine Learning

“…Nonetheless, it must be emphasized that we need a universal defense scheme to detect multiple attacks as much as possible. For this purpose, an artificial-neural-network (ANN)-based universal defense scheme for CVQKD systems was proposed by Mao et al [47].…”

“…In [47], multiple attacks involving three typical attack strategies against CV systems with imperfections of the homodyne detector, the calibration attack, the LO intensity attack, and the saturation attack, were considered, as well as two hybrid attacks [13,48]. Mao et al further investigated some classical features of the pulses and deviations of these features between normal unattacked pulses and abnormal attacked pulses.…”

“…The ANN model for attack detection was trained and tested as depicted in Figure 3 from [47]. Bob's received keys were used as the input and put into the model in order.…”