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arXiv:2401.15898 · Physical Review Research

Mitigation of channel tampering attacks in continuous-variable quantum key distribution

Sebastian P. Kish, Chandra Thapa, Mikhael Sayat, Hajime Suzuki, Josef Pieprzyk, Seyit Ahmet Camtepe

Published Jun 20, 2024• 18 citations
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Abstract

Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying new attack vectors and countermeasures for CV-QKD implementations is important for the continued robustness of CV-QKD. In particular, as CV-QKD relies on a public quantum channel, vulnerability to communication disruption persists from potential adversaries employing denial-of-service (DoS) attacks. Inspired by DoS attacks, this paper introduces a threat in CV-QKD called the channel amplification (CA) attack, wherein Eve manipulates the communication channel through amplification. We specifically model this attack in a CV-QKD optical fiber setup. To counter this threat, we propose a detection and mitigation strategy. Detection involves a machine learning (ML) model based on a decision tree classifier, classifying various channel tampering attacks, including CA and DoS attacks. For mitigation, Bob, postselects quadrature data by classifying the attack type and frequency. Our ML model exhibits high accuracy in distinguishing and categorizing these attacks. The CA attack's impact on the secret key rate (SKR) is explored concerning Eve's location and the relative intensity noise of the local oscillator (LO). The proposed mitigation strategy improves the attacked SKR for CA attacks and, in some cases, for hybrid CA-DoS attacks. Our study marks an application of both ML classification and postselection in this context. These findings are important for enhancing the robustness of CV-QKD systems against emerging threats on the channel. Published by the American Physical Society 2024

DOI
10.1103/physrevresearch.6.023301

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Despite significant advancements in continuous-variable quantum key distribution (CV-QKD), practical CV-QKD systems can be compromised by various attacks. Consequently, identifying new attack vectors and countermeasures for CV-QKD implementations is important for the continued robustness of CV-QKD. In particular, as CV-QKD relies on a public quantum channel, vulnerability to communication disruption persists from potential adversaries employing denial-of-service (DoS) attacks. Inspired by DoS attacks, this paper introduces a threat in CV-QKD called the channel amplification (CA) attack, wherein Eve manipulates the communication channel through amplification. We specifically model this attack in a CV-QKD optical fiber setup. To counter this threat, we propose a detection and mitigation strategy. Detection involves a machine learning (ML) model based on a decision tree classifier, classifying various channel tampering attacks, including CA and DoS attacks. For mitigation, Bob, postselects quadrature data by classifying the attack type and frequency. Our ML model exhibits high accuracy in distinguishing and categorizing these attacks. The CA attack's impact on the secret key rate (SKR) is explored concerning Eve's location and the relative intensity noise of the local oscillator (LO). The proposed mitigation strategy improves the attacked SKR for CA attacks and, in some cases, for hybrid CA-DoS attacks. Our study marks an application of both ML classification and postselection in this context. These findings are important for enhancing the robustness of CV-QKD systems against emerging threats on the channel. Published by the American Physical Society 2024
Due to possible imperfect implementation of laser sources and coherent detectors, continuous-variable quantum key distribution (CV-QKD) in optical networks is vulnerable to quantum attacks, which would significantly degrade the secret key rate (SKR) and compromise the security of the quantum keys. The attackers may steal the data encrypted with the compromised keys, leading to service failure and resource wastage. To tackle it, we propose a quantum attack mitigation resource allocation scheme (QAM-RA) for CV-QKD over optical networks. It corrects the biased channel parameters of the compromised quantum channels by conducting appropriate countermeasures, re-analyzes the SKR, and allocates additional quantum resources to compensate for the loss of SKR. A deep reinforcement learning (DRL) framework based on the Asynchronous Advantage Actor-Critic (A3C) algorithm is developed to determine QAM-RA solutions intelligently. Extensive simulations have been conducted to evaluate the performance of the DRL-assisted QAM-RA scheme in two test networks. Simulation results have confirmed the effectiveness of the proposed scheme in mitigating quantum attacks and reducing user request blocking probabilities.
As continuous-variable quantum key distribution (CV-QKD) systems move toward integration with dense wavelength division multiplexing (DWDM) networks, distinguishing between external faults and malicious attacks becomes crucial for maintaining secure and high-availability key generation. While most existing studies focus on either attack mitigation or fault resilience independently, little attention has been paid to their joint classification. In this work, we present a machine learning-based approach to differentiate between system faults and quantum-level attacks in CV-QKD systems operating under DWDM coexistence. We emulate two practical filter-related faults—shifted filter frequency (SFF) and narrow filter bandwidth (NFB)—and a power-splitting channel tampering attack. Using Bob’s quadrature data (pB, qB) collected from a CV-QKD simulation test setup, we evaluate five classifiers: Gaussian Naive Bayes (GNB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN). We assess performance across various input sample sizes, measuring both classification accuracy and average prediction time. Our results show that while GNB offers the fastest predictions, it suffers from low accuracy. In contrast, DNN achieves near-perfect classification accuracy but at higher computational cost. RF presents a favorable trade-off, offering high accuracy with low latency. This work enables intelligent diagnosis of faults and attacks in telecom-integrated QKD systems, improving the uptime without compromising security.
In practical continuous-variable quantum key distribution system using local local oscillator (LLO CV-QKD), the phase noise related to coherent detection and phase-reference pulse intensity that can be locally calibrated at the receiver side is considered to be trusted noise to improve the performance. However, if the intensity of the phase-reference pulse is not monitored precisely in real-time, a security loophole will be opened for the eavesdropper to launch attacks. To this end, we propose two practical phase-reference pulse intensity attack strategies, where Eve can reduce the trusted phase noise by manipulating the intensity of the phase-reference pulse, thereby hiding her attack on the signal pulse if the total excess noise remains unchanged. The performance of the LLO CV-QKD system under these attacks has been analyzed. We show that precisely monitoring the intensity of the phase-reference pulse in real-time is an essential countermeasure to prevent the proposed attacks. Moreover, an intensity-monitoring scheme for the phase-reference pulse is proposed to strengthen the security of the practical LLO CV-QKD system and make the trusted phase noise model more robust.
In the practical Continuous Variable Quantum Key Distribution (CVQKD) system, there is a large gap between the ideal theoretical model and the actual physical system. There are still some inevitable flaws, which give quantum hackers the opportunity to manipulate the channel in complex communication environments and launch Denial of Service attacks on the quantum channel. Therefore, a DoS attack-aware defense scheme for the CVQKD system based on convolutional neural networks (CNN) is proposed. The simulation results show that the proposed model can effectively detect DoS attacks launched by quantum hackers in CVQKD system in a complex communication environment, and the model has strong robustness due to the addition of the attention mechanism module. In addition, multiple sets of comparative experiments show that compared with the existing artificial neural network model, the CNN-based model has higher accuracy and stability.
Continuous-variable quantum key distribution (CV-QKD) with a real local oscillator (LO) experiences a phase attack security challenge due to the reference pulses transmitted along with quantum signals over an unsecure quantum channel. In this paper, a phase attack on reference pulses of CV-QKD with a real LO is investigated. The performance of the discrete-modulated (DM) CV-QKD under this attack is analyzed analytically and by simulation, and compared with the Gaussian-modulated (GM) CV-QKD. It is found that such an attack by an eavesdropper's, significantly reduce the transmission distance for the GM based CV-QKD, whereas, the DM based CV-QKD shows a high tolerance to high phase noise. Based on phase attack monitoring, a new adaptive modulation scheme is proposed for CV-QKD to adaptively switch between GM and DM so as to maintain the optimum secret key rate.
Continuous-variable quantum key distribution (CV-QKD) is realized with coherent detection and is therefore very suitable for a cost-efficient implementation. The major challenge in CV-QKD is mitigation of laser phase noise at a signal to noise ratio of much less than 0 dB. So far, this has been achieved with a remote local oscillator or with auxiliary signals. For the first time, we experimentally demonstrate that CV-QKD can be performed with a real local oscillator and without auxiliary signals which is achieved by applying Machine Learning methods. It is shown that, with the most established discrete modulation protocol, the experimental system works down to a quantum channel signal to noise ratio of -19.1 dB. The performance of the experimental system allows CV-QKD at a key rate of 9.2 Mbit/s over a fiber distance of 26 km. After remote local oscillator and auxiliary signal aided CV-QKD, this could mark a starting point for a third generation of CV-QKD systems that are even more attractive for a wide implementation because they are almost identical to standard coherent systems.