Classifying Supersonic Frequencies for Active Acoustic Side-Channel Exploitation
Computing side-channel research explores the manner in which physical emanations from systems can be used to reconstruct data. Acoustic side-channels are those physical emanations that produce a sonic frequency that is subsonic, supersonic, or considered in the range of human hearing. Acoustic side-...
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| Main Author: | |
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| Format: | Dissertation |
| Language: | English |
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ProQuest Dissertations & Theses
01-01-2024
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| Online Access: | Get full text |
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| Summary: | Computing side-channel research explores the manner in which physical emanations from systems can be used to reconstruct data. Acoustic side-channels are those physical emanations that produce a sonic frequency that is subsonic, supersonic, or considered in the range of human hearing. Acoustic side-channel attacks (SCAs) are typically performed passively: a listening device captures aural frequencies from a machine via a microphone that are transmitted to the attacker for analysis. Machine learning models have been presented to classify individual keystrokes according to variations in acoustic frequency. Furthermore, the SonarSnoop framework presents a novel active approach that involves both generating and recording aural frequencies acting as a type of sonar system to record physical motion.This research attempts to develop a supervised machine learning model to classify finger motion to collect login credentials typed on a laptop keyboard. The active acoustic side-channel has been used to track two-dimensional finger motion, but three-dimensional finger tracking using active acoustics is novel. The model as trained in this study incorrectly inferred labels on unseen data; however, we found and demonstrated that training with more samples per label may result in greater success during inference. |
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| ISBN: | 9798896072034 |