A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards
This poses a security threat for users of personal devices by enabling practical keystroke classification with off-the-shelf equipment, representing a strong incremental advance in attack methods.
The paper tackled the problem of acoustic side channel attacks on keyboards using deep learning, achieving 95% accuracy with a nearby smartphone microphone and 93% via Zoom, demonstrating high effectiveness without language models.
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.