{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T14:20:40Z","timestamp":1773670840511,"version":"3.50.1"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Digital Threats"],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    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 [\n                    <jats:xref ref-type=\"bibr\">12<\/jats:xref>\n                    ]. 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 [\n                    <jats:xref ref-type=\"bibr\">8<\/jats:xref>\n                    ,\n                    <jats:xref ref-type=\"bibr\">12<\/jats:xref>\n                    ,\n                    <jats:xref ref-type=\"bibr\">14<\/jats:xref>\n                    ]. Machine learning models have been presented to classify individual keystrokes according to variations in acoustic frequency [\n                    <jats:xref ref-type=\"bibr\">2<\/jats:xref>\n                    ]. 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 [\n                    <jats:xref ref-type=\"bibr\">7<\/jats:xref>\n                    ]. 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.\n                  <\/jats:p>","DOI":"10.1145\/3786765","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T14:20:18Z","timestamp":1766758818000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Classifying Supersonic Frequencies for Active Acoustic Side-Channel Exploitation"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1580-9753","authenticated-orcid":false,"given":"Destin","family":"Hinkel","sequence":"first","affiliation":[{"name":"School of Computing, University of South Alabama, Mobile, Alabama, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1308-5942","authenticated-orcid":false,"given":"George","family":"Clark","sequence":"additional","affiliation":[{"name":"School of Computing, University of South Alabama, Mobile, Alabama, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5266-7470","authenticated-orcid":false,"given":"J. 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