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

• URL: http://arxiv.org/abs/2308.01074v1
• Date: Wed, 2 Aug 2023 10:51:36 GMT
• Title: A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards
• Authors: Joshua Harrison, Ehsan Toreini, Maryam Mehrnezhad
• Abstract summary: This paper presents 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. We discuss a series of mitigation methods to protect users against these series of attacks.
• Score: 6.230751621285321
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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