A Signal-Denoising Method for Electromagnetic Leakage from USB Keyboards

Abstract

USB keyboards are commonly used as computer input devices and inevitably generate electromagnetic (EM) leakage signals during their operation, which carry input information. However, due to the weak energy of a keyboard’s EM signal and the small amount of effective information, the received leakage signal is often characterized by a low signal-to-noise ratio (SNR). This low SNR affects the subsequent detection and restoration of the information. In order to solve this problem, this paper proposes a denoising method for USB keyboard EM leakage signals and designs a self-attentive denoising adversarial network (SADAN) based on generative adversarial networks (GANs). The denoiser continuously enhances the denoising ability during the generative adversarial process, and the self-attention mechanism enables it to better learn the dependencies of the keyboard EM leak signal sequences, modelling the long-range relationships between the sequence sample points and reducing the impact of the number of network layers on the relationship acquisition. The method achieves noise suppression in the keyboard leakage signal, improving its SNR while preserving the effective information in the leakage signal and finally obtaining a denoised leakage signal that can be effectively restored to the information.