Empirical Risk-aware Machine Learning on Trojan-Horse Detection for Trusted Quantum Key Distribution Networks
Hong-fu Chou, Thang Xuan Vu, Ilora Maity, Garces-Socarras, Luis M., Gonzalez-Rios, Jorge L., Merlano-Duncan, Juan Carlos, Ma, Sean Longyu, Symeon Chatzinotas, Björn Ottersten
Quantum key distribution (QKD) is a cryptographic technique that leverages principles of quantum mechanics to offer extremely high levels of data security during transmission. It is well acknowledged for its capacity to accomplish provable security. However, the existence of a gap between theoretical concepts and practical implementation has raised concerns about the trustworthiness of QKD networks. In order to mitigate this disparity, we propose the implementation of risk-aware machine learning techniques that present risk analysis for Trojan-horse attacks over the time-variant quantum channel. The trust condition presented in this study aims to evaluate the offline assessment of safety assurance by comparing the risk levels between the recommended safety borderline. This assessment is based on the risk analysis conducted. Furthermore, the proposed trustworthy QKD scenario demonstrates its numerical findings with the assistance of a state-of-the-art point-to-point QKD device, which operates over optical quantum channels spanning distances of 1m, 1km, and 30km. Based on the results from the experimental evaluation of a 30km optical connection, it can be concluded that the QKD device provided prior information to the proposed learner during the non-existence of Eve's attack. According to the optimal classifier, the defensive gate offered by our learner possesses the capability to identify any latent Eve attacks, hence effectively mitigating the risk of potential vulnerabilities. The Eve detection probability is provably bound for our trustworthy QKD scenario.
Quantum key distribution (QKD) is a cryptographic technique that leverages principles of quantum mechanics to offer extremely high levels of data security during transmission. It is well acknowledged for its capacity to accomplish provable security. However, the existence of a gap between theoretical concepts and practical implementation has raised concerns about the trustworthiness of QKD networks. In order to mitigate this disparity, we propose the implementation of risk-aware machine learning techniques that present risk analysis for Trojan-horse attacks over the time-variant quantum channel. The trust condition presented in this study aims to evaluate the offline assessment of safety assurance by comparing the risk levels between the recommended safety borderline. This assessment is based on the risk analysis conducted. Furthermore, the proposed trustworthy QKD scenario demonstrates its numerical findings with the assistance of a state-of-the-art point-to-point QKD device, which operates over optical quantum channels spanning distances of 1m, 1km, and 30km. Based on the results from the experimental evaluation of a 30km optical connection, it can be concluded that the QKD device provided prior information to the proposed learner during the non-existence of Eve's attack. According to the optimal classifier, the defensive gate offered by our learner possesses the capability to identify any latent Eve attacks, hence effectively mitigating the risk of potential vulnerabilities. The Eve detection probability is provably bound for our trustworthy QKD scenario.
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 categorybased 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-theart 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.
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.
ABSTRACT The emergence of quantum computing poses significant risks to the security of modern communication networks as it breaks today's public‐key cryptographic algorithms. Quantum key distribution (QKD) offers a promising solution by harnessing the principles of quantum mechanics to establish secure keys. However, practical QKD implementations remain vulnerable to hardware imperfections and advanced attacks such as photon number splitting and Trojan‐Horse attacks. In this work, we investigate the potential of quantum machine learning (QML) to detect popular QKD attacks. In particular, we propose a hybrid quantum long short‐term memory (QLSTM) model to improve the detection of common QKD attacks. By combining quantum‐enhanced learning with classical deep learning, the model captures complex temporal patterns in QKD data, improving detection accuracy. To evaluate the proposed model, we introduce a realistic QKD dataset that simulates typical QKD operations, along with seven attack scenarios: Intercept‐and‐resend, photon‐number splitting (PNS), Trojan‐Horse attacks on the random number generator (RNG), detector blinding, wavelength‐dependent Trojan‐Horse and combined attacks. The dataset includes quantum security metrics such as quantum bit error rate (QBER), measurement entropy, signal and decoy loss rates and time‐based metrics, ensuring an accurate representation of real‐world conditions. Our results demonstrate the promising performance of the quantum machine learning approach compared to traditional classical machine learning models, highlighting the potential of hybrid techniques to enhance the security of future quantum communication networks. The proposed hybrid QLSTM model achieved an accuracy of 94.7.0% after 50 training epochs, outperforming classical deep learning models such as LSTM, CNN, ANN, random forest and RNN. However, our evaluation is conducted on a semi‐realistic, simulation‐generated decoy‐state BB84 dataset with simplified models of attacks and device imperfections, so the reported performance should be interpreted as a proof‐of‐concept rather than a final assessment on field‐deployed QKD systems.
Quantum key distribution (QKD) leverages the principles of quantum mechanics to provide theoretically unconditional security for cryptographic key sharing. However, practical implementations remain vulnerable due to potential security loopholes at both the source and detection sides of QKD systems. The side-channel-secure (SCS) protocol addresses these challenges by encoding bits in vacuum and non-vacuum states and introducing a third-party measurement node, thereby repelling attacks targeting the detection side as well as external lab attacks on the source side. In this work, we consider the state-dependent correlated errors and Trojan-horse attack while preserving the SCS protocol’s key advantage—-specifically, requiring only upper bounds on intensity characterization without needing a full description of quantum states in infinite dimensions. Numerical results demonstrate that when the reflected light intensity from Trojan-horse attacks falls below 10 −6 , Eve can scarcely extract additional key information from the reflections. This work makes the SCS protocol more practical.
An eavesdropper Eve may probe a quantum key distribution (QKD) system by sending a bright pulse from the quantum channel into the system and analyzing the back-reflected pulses. Such Trojan-horse attacks can breach the security of the QKD system, if appropriate safeguards are not installed or if they can be fooled by the Eve. We present a risk analysis of such attacks based on extensive spectral measurements, such as transmittance, reflectivity, and detection sensitivity of some critical components used in a typical QKD systems. Our results indicate the existence of wavelength regimes, where the attacker gains considerable advantage as compared to launching an attack at 1550 nm. We also propose countermeasures to reduce the risk of such attacks.
Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.