Keystroke transcription from acoustic emanations using continuous wavelet transform

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2022-9

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Özkan, Abdullah

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One of the most common methods of communication is written communication. Written communication has been found in various forms over the years and has changed shape with technical and technological developments. Today, written communication has shifted to digital media and keyboards have become one of the most frequently used entry points. This makes keyboards a critical node in the flow of information. There are several ways in which information entered through the keyboard can leak. Acoustic propagation is one of these leakage pathways. In the literature, various approaches have been proposed for this attack type, which aim to process the keystroke sounds and capture the information. However, the usage of continous wavelet transform for this purpose has not been explored before. Continous wavelet transform provides better resolution in both time and frequency for impulse-like signals. Therefore, this transformation is better suited to the analysis of keystroke sounds than other conventional transforms. In this thesis, we propose a method based on the continuous wavelet transform for transcription of keystrokes from the acoustic emanations of a keyboard, using wave files as input and recovering the written text as output with up to 57.2% accuracy. Initially a dataset was generated by recording keystroke sounds of 38 keys pressed multiple times in different ways. These were then analysed using the continuous wavelet transform in order to detect, segregate and obtain features of keystroke sounds. Various classification algorithms were tested and performances were recorded. Recommendations were made for improving the system output by using dictionaries and language models, as well as the information obtained from the confusion matrix itself.

Subject Keywords

Acoustic Propagation, Text Extraction, Continuous Wavelet Transform, Keyboard, Machine Learning

URI

https://hdl.handle.net/11511/99509

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Graduate School of Informatics, Thesis

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Citation Formats

A. Özkan, “Keystroke transcription from acoustic emanations using continuous wavelet transform,” M.S. - Master of Science, Middle East Technical University, 2022.