Robust keystroke transcription from the acoustic side-channel
David A. Slater, Scott Novotney, Jessica Moore, Sean Morgan, Scott Tenaglia · 2019
The acoustic emanations from keyboards provide a side-channel attack from which an attacker can recover sensitive user information, such as passwords and personally identifiable information. Previous work has shown the feasibility of these attacks given isolated key strokes, but has not demonstrated robust keystroke detection and segmentation in the presence of realistic noise and fast typing speeds. Common problems include noises like doors closing or speech as well as overlapping keystroke waveforms. Prior work has assumed that isolating the waveform of individual key strokes can be achieved with near 100% accuracy, but we show that these techniques generate a large number of misses and false positives, drastically impacting the downstream keystroke classification task.