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

Autor:	Harrison, Joshua, Toreini, Ehsan, Mehrnezhad, Maryam
Rok vydání:	2023
Předmět:	Computer Science - Cryptography and Security Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/EuroSPW59978.2023.00034
Popis:	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. Comment: This paper was already accepted in 2023 IEEE European Symposium on Security and Privacy Workshop, SiLM'23 (EuroS&PW)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.01074 Zobrazit plný text záznamu View this record from Arxiv