Noise-Resistant Feature-Aware Attack Detection Using Quantum Machine Learning

TL;DR

This paper introduces a quantum machine learning-based attack detection framework for CV-QKD systems, utilizing QSVM and QNN models to effectively identify known and unknown quantum attacks even under noisy conditions.

Contribution

The paper develops a noise-resistant, feature-aware quantum attack detection framework using QSVM and QNN models tailored for high-rate CV-QKD systems, enhancing security against quantum attacks.

Findings

All twelve QML variants effectively detect known and unknown attacks.

QSVM outperforms QNN in attack detection accuracy.

The framework demonstrates robustness under various noise conditions.

Abstract

Continuous-variable quantum key distribution (CV-QKD) is a quantum communication technology that offers an unconditional security guarantee. However, the practical deployment of CV-QKD systems remains vulnerable to various quantum attacks. In this paper, we propose a quantum machine learning (QML)-based attack detection framework (QML-ADF) that safeguards the security of high-rate CV-QKD systems. In particular, two alternative QML models -- quantum support vector machines (QSVM) and quantum neural networks (QNN) -- are developed to perform noise-resistant and feature-aware attack detection before conventional data postprocessing. Leveraging feature-rich quantum data from Gaussian modulation and homodyne detection, the QML-ADF effectively detects quantum attacks, including both known and unknown types defined by these distinctive features. The results indicate that all twelve distinct QML variants for both QSVM and QNN exhibit remarkable performance in detecting both known and previously undiscovered quantum attacks, with the best-performing QSVM variant outperforming the top QNN counterpart. Furthermore, we systematically evaluate the performance of the QML-ADF under various physically interpretable noise backends, demonstrating its strong robustness and superior detection performance. We anticipate that the QML-ADF will not only enable robust detection of quantum attacks under realistic deployment conditions but also strengthen the practical security of quantum communication systems.