Detection of wake word jamming

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dc.contributor.authorSagi, Prathyushaen
dc.contributor.authorSankar, Arunen
dc.contributor.authorRoedig, Utzen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-01-17T15:56:38Z
dc.date.available2025-01-17T15:56:38Z
dc.date.issued2024-11-22en
dc.description.abstractPersonal Voice Assistants (PVAs) such as Apple's Siri, Amazon's Alexa and Google Home continuously monitor the acoustic environment for a wake word to start interaction with the user. However, the wake word detection is susceptible to disruptions caused by acoustic interference. Interference might be by noise (e.g. background music, chatter, engine sounds) or a targeted jamming signal designed to disrupt PVA operations. As PVAs are increasingly used for critical applications such as medicine and the military, it is necessary to identify an attack. In this work, we re-design the wake word detection algorithm such that it is not only robust against an attack but is also able to identify an attack. Only if it is possible to identify an ongoing attack it is possible to employ appropriate countermeasures, i.e. remove the attacker. We modify the wake word detection model to function as a three-class classifier that accurately differentiates between clean wake words, wake words mixed with jamming signals and non-wake words. We further improve the classification results by examining the Direction of Arrival (DOA) and the Short Time Energy (STE ) of the audio signal. DOA and STE information is usually available on off-the-shelf PVA which enables implementation of the proposed methods on existing devices.en
dc.description.sponsorshipScience Foundation Ireland (13/RC/2077_P2)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSagi, P., Sankar, A. and Roedig, U. (2024) 'Detection of wake word jamming', Proceedings of the Sixth Workshop on CPS&IoT Security and Privacy, Salt Lake City, UT, USA, October 14-18, pp. 134-141. https://doi.org/10.1145/3690134.3694825en
dc.identifier.doihttps://doi.org/10.1145/3690134.3694825en
dc.identifier.endpage141en
dc.identifier.isbn979-8-4007-1244-9en
dc.identifier.startpage134en
dc.identifier.urihttps://hdl.handle.net/10468/16843
dc.language.isoenen
dc.publisherAssociation for Computing Machineryen
dc.relation.ispartofProceedings of the Sixth Workshop on CPS&IoT Security and Privacy, Salt Lake City, UT, USA, October 14-18 2024en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Frontiers for the Future::Award/19/FFP/6775/IE/Personal Voice Assistant Security and Privacy/en
dc.rights© 2024, the Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectPersonal Voice Assistant (PVA)en
dc.subjectAutomatic Speech Recognition (ASR)en
dc.subjectWake Word Recognitionen
dc.subjectAcoustic Denial of Service (DoS)en
dc.subjectAcoustic Jammingen
dc.subjectAdversarial Trainingen
dc.subjectShort Time Energy (STE)en
dc.subjectDirection of Arrival (DOA)en
dc.titleDetection of wake word jammingen
dc.typeConference itemen
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