Abstract While quantum key distribution (QKD) is a theoretically secure way of growing quantum-safe encryption keys, many practical implementations are challenged due to various open attack vectors, resulting in many variations of QKD protocols. Side channels are one such vector that allows a passive or active eavesdropper to obtain QKD information leaked through practical devices. This paper assesses the feasibility and implications of extracting the raw secret key from far-field radiated emissions from the single-photon avalanche diodes used in a BB84 QKD quad-detector receiver. Enhancement of the attack was also demonstrated through the use of deep-learning model to distinguish radiated emissions due to the four polarized encoding states. To evaluate the severity of such side-channel attack, multi-class classification based on raw-data and pre-processed data is implemented and assessed. Results show that classifiers based on both raw-data and pre-processed features can discern variations of the electromagnetic emissions caused by specific orientations of the detectors within the receiver with an accuracy higher than 90%. This research proposes machine learning models as a technique to assess EM information leakage risk of QKD and highlights the feasibility of side-channel attacks in the far-field region, further emphasizing the need to utilise mechanisms to avoid electromagnetic radiation information leaks and measurement-device-independent QKD protocols.
Abstract While quantum key distribution (QKD) is a theoretically secure way of growing quantum-safe encryption keys, many practical implementations are challenged due to various open attack vectors, resulting in many variations of QKD protocols. Side channels are one such vector that allows a passive or active eavesdropper to obtain QKD information leaked through practical devices. This paper assesses the feasibility and implications of extracting the raw secret key from far-field radiated emissions from the single-photon avalanche diodes used in a BB84 QKD quad-detector receiver. Enhancement of the attack was also demonstrated through the use of deep-learning model to distinguish radiated emissions due to the four polarized encoding states. To evaluate the severity of such side-channel attack, multi-class classification based on raw-data and pre-processed data is implemented and assessed. Results show that classifiers based on both raw-data and pre-processed features can discern variations of the electromagnetic emissions caused by specific orientations of the detectors within the receiver with an accuracy higher than 90%. This research proposes machine learning models as a technique to assess EM information leakage risk of QKD and highlights the feasibility of side-channel attacks in the far-field region, further emphasizing the need to utilise mechanisms to avoid electromagnetic radiation information leaks and measurement-device-independent QKD protocols.
While quantum key distribution (QKD) protocols are proven secure based on fundamental physical laws, side channels may allow an eavesdropper to obtain information unnoticeably. We present a profiled [1] side-channel attack using a deep convolutional neural network to analyze the classical, radio-frequency electromagnetic emissions from the electronics of a QKD sender. At a few centimeters from the device, we are able to recover virtually all information about the secret key. Furthermore, we can still observe traces of electromagnetic radiation from the device at distances of a few meters using a wideband antenna. Our methods are designed to be easily adaptable and may serve as a starting point for assessing the presence of this side channel for other devices.
The security provided by Quantum Key Distribution (QKD) can be strongly compromised by interception of the raw and sifted key through side channels, such as the practical characteristics of electronic components used in the transmitter and receiver modules. Some out-of-band electromagnetic attacks have already been identified and tested in components used in QKD, such as quantum random number generators. In this presentation, we explore out-of-band electromagnetic attacks of other components used in a quantum receiver, such as Single Photon Avalanche Diodes (SPADs), and the time-correlated single-photon counting module. We measured the electromagnetic (EM) radiated emissions of the components to quantify the emanation levels and evaluate the vulnerability that this QKD side channel may present. The test was conducted in an anechoic chamber up to 1 GHz, at 3 m distance, and rotating the SPAD to provide radiation from four azimuth angles. Results show that measurable radiated pulses are generated by the SPAD in this frequency range due to dark count pulses and due to incoming Single-Photon level pulses. Dark counts of few kHz and Single-Photon level counts of hundreds of kHz were considered in the tests. EM radiation frequency bands with main emissions and electric field strengths are identified for both operation conditions.
Quantum Key Distribution (QKD) promises information-theoretic security based on the fundamental laws of quantum mechanics. However, practical implementations deviate from idealized models and are therefore susceptible to side-channel attacks that exploit physical imperfections rather than theoretical vulnerabilities. Over the past two decades, a wide range of attacks have been demonstrated against QKD systems, including those targeting detectors, sources, timing information, power consumption, and electromagnetic emissions. This paper addresses a selection of a few side-channel attacks reported in the literature, classifying them according to their underlying physical mechanisms and attack strategies. We analyze their impact on the security of different QKD protocols and platforms, and highlight how experimental demonstrations have shaped the community's understanding of implementation security. In addition, we present and evaluate mitigation techniques proposed to date, such as device-independent and measurement-device-independent QKD, hardware countermeasures, monitoring strategies, and so on. We also identify open challenges and promising directions for enhancing the robustness of future quantum communication systems.
The rapid development of quantum computing threatens the traditional cryptographic schemes and suggests that the best alternative solution for ensuring the security of key exchange is Quantum Key Distribution (QKD).Despite the information-theoretic security guarantee, the practical implementations of QKD present vulnerabilities that were derived from hardware flaws and the exploitation of the quantum channel.This thesis is intended to address security vulnerabilities by analyzing and proposing an innovative architecture for the identification and mitigation of quantum attacks, via classification techniques of Quantum Machine Learning and the use of the metrics of Quantitative Risk Analysis.The study begins with the concepts of quantum information, the functionality of basic QKD protocols (BB84, B92, E91) and the mathematical modeling of Implementation-level attacks and side-channel attacks in quantum systems.Subsequently, the operational processes of Quantum Support Vector Machine (QSVM) and the Variational Quantum Classifier (VQC) are analyzed during the training phase.Based on the theoretical background of this study, QML-IDPS architecture is proposed.Threat detection and classification are performed using the QSVM and VQC classifiers, while risk metrics are utilized and fed into a classical Intrusion Prevention System (IPS) to support the final decision-making process for the prevention of quantum threats.The final key validated by the threat assessment mechanisms, is then used for data encryption with a classical cryptographic algorithm, such as AES-256.The proper functionality of QML-IDPS architecture is verified with the mathematical Intercept-Resend Use Case Scenario, which basically proves the successful classification and prevention of system anomalies.In conclusion, it is confirmed that the proposed QML-IDPS architecture demonstrates that Quantum Machine Learning combined with Quantitative Risk Analysis can enhance the security and reliability of QKD infrastructures.
Quantum key distribution (QKD) is a technique that allows two distant parties to distribute and share a common secret, which then can be used as a cryptographic key. While mathematical proofs verify the security of perfectly implemented systems, imperfections in real devices allow attackers to retrieve information. This study uses machine-learning techniques to investigate information leakage via radio-frequency emissions of QKD device electronics. The approach allows researchers and engineers to harden devices against attacks.
The security of current key exchange protocols such as Diffie-Hellman key exchange is based on the hardness of number theoretic problems. However, these key exchange protocols are threatened by weak random number generators, advances to CPU power, a new attack from the eavesdropper, and the emergence of a quantum computer. Quantum Key Distribution (QKD) addresses these challenges by using quantum properties to exchange a secret key without the risk of being intercepted. Recent developments on the QKD system resulted in a stable key generation with fewer errors so that the QKD system is rapidly becoming a solid commercial proposition. However, although the security of the QKD system is guaranteed by quantum physics, its careless implementation could make the system vulnerable. In this paper, we proposed the first side-channel attack on plug-and-play QKD system. Through a single electromagnetic trace obtained from the phase modulator on Alice's side, we were able to classify the electromagnetic trace into four classes, which corresponds to the number of bit and basis combination in the BB84 protocol. We concluded that the plug-and-play QKD system is vulnerable to side-channel attack so that the countermeasure must be considered.