Keystroke Transcription from Acoustic Emanations Using Continuous Wavelet Transform

Acoustic propagation is a notable pathway, enabling information input via a keyboard to potentially leak. This type of attack, which leverages the processing of keystroke sounds to capture data, has been the subject of various proposed methodologies. However, the application of continuous wavelet transforms for this purpose remains largely unexplored. The continuous wavelet transform provides better resolution in both time and frequency for impulse-like signals. As such, this transformation proves more effective for analyzing keystroke sounds in comparison to conventional transform methods. We propose a method based on machine learning to analyze features. This process involves transcribing keystrokes from the acoustic emanations of a keyboard, utilizing wave files as input. Consequently, this allows the recovery of pressed keys as output, achieving an accuracy rate of up to 80.3%.