Deep-Learning-Based Radio-Frequency Side-Channel Attack on Quantum Key Distribution

Abstract

Quantum key distribution (QKD) protocols are proven secure based on fundamental physical laws, however, the proofs consider a well-defined setting and encoding of the sent quantum signals only. Side channels, where the encoded quantum state is correlated with properties of other degrees of freedom of the quantum channel, allow an eavesdropper to obtain information unnoticeably as demonstrated in a number of hacking attacks on the quantum channel. Yet, also classical radiation emitted by the devices may be correlated, leaking information on the potential key, especially when combined with novel data analysis methods. We here demonstrate a side-channel attack using a deep convolutional neural network to analyze the recorded classical, radio-frequency electromagnetic emissions. Even at a distance of a few centimeters from the electronics of a QKD sender employing frequently used electronic components we are able to recover virtually all information about the secret key. Yet, as shown here, countermeasures can enable a significant reduction of both the emissions and the amount of secret key information leaked to the attacker. Our analysis methods are independent of the actual device and thus provide a starting point for assessing the presence of classical side channels in QKD devices.