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

TL;DR

This paper demonstrates a practical deep learning-based acoustic side channel attack on keyboards using smartphones, achieving high accuracy in keystroke classification and highlighting security risks with off-the-shelf devices.

Contribution

It introduces a novel, practical attack method using deep learning and common devices to classify keystrokes with high accuracy, emphasizing real-world security threats.

Findings

Achieved 95% accuracy with microphone recordings

Achieved 93% accuracy with Zoom recordings

Proved practicality of attacks with off-the-shelf equipment

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.