Deep Learning Based TEMPEST Attacks on a Quantum Key Distribution Sender
Adomas Baliuka, Markus Stöcker, Michael Auer, Peter Freiwang, Harald Weinfurter, Lukas Knips · 2023
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.