Hidden-Markov-model-based calibration-attack recognition for continuous-variable quantum key distribution

Mao, Yiyu; Wang, Yijun; Huang, Wei‐Ping; Qin, Hao; Huang, Duan; Guo, Ying

doi:10.1103/physreva.101.062320

Cited by 21 publications

(10 citation statements)

References 34 publications

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“…The other typical attack is the calibration attack. A hidden Markov-model (HMM) was utilized to detect the calibration attack in real CV systems by Mao et al [42].…”

Section: Countermeasures On a Targeted Attackmentioning

confidence: 99%

“…After analysis, Mao et al [42] found that the variation of the measured quadrature values reflected whether the calibration attack was performed. However, Bob's measurement values are affected by environmental disturbances, the drawbacks of devices Normally, a well-trained HMM will output a high probability value for normal data and a low probability value for attacked data.…”

Section: Countermeasures On a Targeted Attackmentioning

confidence: 99%

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Secure Continuous-Variable Quantum Key Distribution with Machine Learning

2021

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Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied.

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“…The other typical attack is the calibration attack. A hidden Markov-model (HMM) was utilized to detect the calibration attack in real CV systems by Mao et al [42].…”

Section: Countermeasures On a Targeted Attackmentioning

confidence: 99%

Section: Countermeasures On a Targeted Attackmentioning

confidence: 99%

Secure Continuous-Variable Quantum Key Distribution with Machine Learning

2021

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“…Thus it is necessary to detect multiple attacks. The quantum attack can be detected by some machine learning strategies, such as k-nearest neighbors (KNN) [25], support vector machines (SVM) [26], ensemble learning [27], hidden Markov models (HMM) [28], and neural networks (NN) [29,30]. These strategies can automatically detect and classify some known attacks by learning feature distribution instead of simple thresholds; however, there is also a drawback: the previous detection scheme was to select the one with the highest probability from different types of attacks without considering the coexistence of multiple attacks in practice.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD

Huang

2022

Photonics

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The security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the general detection model emerges to improve the universality of detection; however, current universal detection methods only consider the independent existence of attacks but ignore the possible coexistence of multiple attacks in reality. Here, we propose two multi-attack neural network detection models to handle the coexistence of multiple attacks. The models adopt two methods in multi-label learning: binary relevance (BR) and label power (LP) to deal with the coexistence of multiple attacks and can identify attacks in real-time by autonomously learning the features of known attacks in a deep neural network. Further, we improve the model to detect unknown attacks simultaneously. The experimental results show that the proposed scheme can achieve high-precision detection for most known and unknown attacks without reducing the key rate and maximum transmission distance.

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“…The calibration attack may be implemented by modifying the shape of the local oscillator (LO) pulse [24]. Consequently, several countermeasures have been proposed to counteract the effects of the LO calibration attack and the wavelength attacks [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Counteracting a Saturation Attack in Continuous-Variable Quantum Key Distribution Using an Adjustable Optical Filter Embedded in Homodyne Detector

Mao

et al. 2022

Entropy

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A saturation attack can be employed for compromising the practical security of continuous-variable quantum key distribution (CVQKD). In this paper, we suggest a countermeasure approach to resisting this attack by embedding an adjustable optical filter (AOF) in the CVQKD system. Numerical simulations illustrate the effects of the AOF-enabled countermeasure on the performance in terms of the secret key rate and transmission distance. The legal participants can trace back the information that has been eavesdropped by an attacker from the imperfect receiver, which indicates that this approach can be used for defeating a saturation attack in practical quantum communications.

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Hidden-Markov-model-based calibration-attack recognition for continuous-variable quantum key distribution

Cited by 21 publications

References 34 publications

Secure Continuous-Variable Quantum Key Distribution with Machine Learning

Secure Continuous-Variable Quantum Key Distribution with Machine Learning

Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD

Counteracting a Saturation Attack in Continuous-Variable Quantum Key Distribution Using an Adjustable Optical Filter Embedded in Homodyne Detector

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