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.
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.
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.
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.
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.
A newfound security breach in the physical nature of single photon detectors that are generally used in quantum key distribution is explained, we found that the bit contents of a quantum key transmission system can be intercepted from far away by exploiting the ultrawideband electromagnetic signals radiated from hi-voltage avalanche effect of single photon detectors. It means that in fact any Geiger mode avalanche photodiode that is used inside single photon detectors systematically acts like a downconverter that converts the optical-wavelength photons to radio-wavelength photons that can be intercepted by an antenna as side channel attack. Our experiment showed that the radiated waveforms captured by the antenna can be used as a fingerprint. These finger prints were fed to a deep learning neural network as training data, and after training the neural network was able to clone the bit content of quantum transmission.
Quantum key distribution (QKD) is sometimes marketed as secure against computational attacks, as its security is based on the laws of quantum mechanics for detecting eavesdroppers, rather than the difficulty of mathematical problems. However, this security guarantee applies only to the quantum channel itself, and the physical hardware implementing these systems remain vulnerable. The electrical components controlling these systems generate electromagnetic emissions when active, and if those emissions carry information such as the polarization states being encoded, an attacker could recover key-relevant information without ever interacting with the quantum channel, bypassing the security guarantees of the protocol entirely. This work investigates whether such a vulnerability exists in a commercial QKD transmitter unit by applying various unsupervised and supervised machine-learning methods to near-field electromagnetic measurements, with the aim of determining whether device-level parameters and the prepared polarization states can be extracted from the observed emissions alone. The developed unsupervised machine-learning methods were unable to recover the distribution of polarization states from the electromagnetic measurements, partitioning a single dominant cluster into arbitrary groupings, rather than finding separate clusters. When trained on files with various distributions of key and check bases, the Ridge regression model exhibited strong predictive capability, accurately following the proportions of D-states during leave-one-out cross-validation, with a mean absolute error (MAE) of 4.9% and an R2 score of 0.946. During testing on new, unseen files, the MAE increased to 11.3% and the R2 score dropped to -0.12, largely attributed to a single file, with the remaining test files producing results more consistent with the cross-validation performance. Similarly, a convolutional neural network trained on fixed five-symbol sequences achieved an accuracy of 41.13% and a F1-score of 36.46%, which for the given symbol space is significantly above random guessing of 33.33%.
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.