With recent developments in deep learning, the ubiquity of micro-phones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever. This paper presents a practical implementation of a state-of-the-art deep learning model in order to classify laptop keystrokes, using a smartphone integrated microphone. When trained on keystrokes recorded by a nearby phone, the classifier achieved an accuracy of 95%, the highest accuracy seen without the use of a language model. When trained on keystrokes recorded using the video-conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium. Our results prove the practicality of these side channel attacks via off-the-shelf equipment and algorithms. We discuss a series of mitigation methods to protect users against these series of attacks.
@article{arxiv.2308.01074,
title = {A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards},
author = {Joshua Harrison and Ehsan Toreini and Maryam Mehrnezhad},
journal= {arXiv preprint arXiv:2308.01074},
year = {2023}
}
Comments
This paper was already accepted in 2023 IEEE European Symposium on Security and Privacy Workshop, SiLM'23 (EuroS&PW)