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Keystroke Transcription from Acoustic Emanations Using Continuous Wavelet Transform
Date
2024-01-01
Author
Ozkan, Abdullah
Günel Kılıç, Banu
Acartürk, Cengiz
Metadata
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Acoustic propagation is a notable pathway, enabling information input via a keyboard to potentially leak. This type of attack, which leverages the processing of keystroke sounds to capture data, has been the subject of various proposed methodologies. However, the application of continuous wavelet transforms for this purpose remains largely unexplored. The continuous wavelet transform provides better resolution in both time and frequency for impulse-like signals. As such, this transformation proves more effective for analyzing keystroke sounds in comparison to conventional transform methods. We propose a method based on machine learning to analyze features. This process involves transcribing keystrokes from the acoustic emanations of a keyboard, utilizing wave files as input. Consequently, this allows the recovery of pressed keys as output, achieving an accuracy rate of up to 80.3%.
Subject Keywords
Acoustic Propagation
,
Continuous Wavelet Transform
,
Text Extraction
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85192345907&origin=inward
https://hdl.handle.net/11511/109683
DOI
https://doi.org/10.1007/978-981-97-2458-1_1
Conference Name
5th International Conference on Machine Learning for Cyber Security, ML4CS 2023
Collections
Graduate School of Informatics, Conference / Seminar
Citation Formats
IEEE
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MLA
BibTeX
A. Ozkan, B. Günel Kılıç, and C. Acartürk, “Keystroke Transcription from Acoustic Emanations Using Continuous Wavelet Transform,” Yanuca Island, Fiji, 2024, vol. 14541 LNCS, Accessed: 00, 2024. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85192345907&origin=inward.