A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards

Abstract

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.

This paper was already accepted in 2023 IEEE European Symposium on Security and Privacy Workshop, SiLM'23 (EuroS&PW)

References in corpus (3)

• CoAtNet: Marrying Convolution and Attention for All Data Sizes
• Hearing your touch: A new acoustic side channel on smartphones
• Don't Skype & Type! Acoustic Eavesdropping in Voice-Over-IP