# Deep Learning-Based Acoustic Side Channel Attack on Keyboards

Source: https://arxiv.org/abs/2308.01074
Kiwi News: https://news.kiwistand.com/stories/Deep-Learning-Based-Acoustic-Side-Channel-Attack-on-Keyboards?index=0x64cfd9fdfe1da023effdad98d54b3c5b6f7c9b9e9635487fe7aa8a33d2cf3f805fab12cc
Submitted by m-j-r.eth on 2023-08-06 UTC, 2 upvotes, 0 comments

## Summary

Harrison, Toreini and Mehrnezhad present a practical acoustic side channel attack that uses a deep learning model to classify laptop keystrokes from sound captured by a smartphone microphone. The authors report 95% accuracy when the classifier is trained on keystrokes recorded by a nearby phone, which they describe as the highest accuracy seen without a language model. Training on keystrokes recorded through the video-conferencing software Zoom yielded 93% accuracy, which the paper describes as a new best for that medium. The authors conclude that the attacks are practical using off-the-shelf equipment and algorithms, and they discuss mitigation methods.

## Article

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

Authors: Joshua Harrison, Ehsan Toreini, Maryam Mehrnezhad

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.