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Detecting Trojan-Horse Attacks in Practical QKD via Gaussian Mixture Modeling-Assisted QBER Goodness-of-Fit Analysis

Hong-fu Chou, H. Peng, Thang Xuan Vu, Ilora Maity, Youssouf Drif, Luis Manuel Garcés-Socarrás, Jorge Luis González Ríos, Juan Carlos Merlano Duncán, Sean Longyu Ma, Symeon Chatzinotas

Published Dec 8, 2025• 0 citations

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Abstract

Quantum key distribution (QKD) offers exceptionally high levels of data security during transmission by using principles of quantum physics. It is renowned for its provable security features. However, a gap between theoretical models and real-world applications, known as quantum hacking, challenges the reliability of QKD networks. Trojan-horse attacks represent a significant threat to the Bob subsystem in QKD, allowing Eve to infer Alice’s basis choices through back-reflected pulses. This can compromise security without detection in severe cases, especially when quantum bit error rates (QBER) fall below the abort threshold. The proposed method combines a category-based Gaussian Mixture Model (GMM) with the Kolmogorov-Smirnov test to estimate the posterior QBER distribution and assess risks in practical QKD systems. By processing the QBER, the approach also evaluates the dependability of the QKD scenario. Numerical results are presented using a state-of-the-art point-to-point QKD device operating over optical quantum channels of 1 m, 1 km, and 30 km lengths. The results of the experimental analysis of a 30 km optical link suggest that the QKD device provided prior information to the proposed learner. Consequently, our proposed trustworthy monitor offers a defensive mechanism that identifies potential Eve attacks, effectively mitigating the risk of security vulnerabilities.

DOI: 10.1109/globecom59602.2025.11431935

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