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PLIO: Physical Layer Identification using One-shot Learning

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorHazra, Saptarshi
dc.contributor.authorVoigt, Thiemo
dc.contributor.authorYan, Wenqing
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2022-01-18T12:28:07Z
dc.date.available2022-01-18T12:28:07Z
dc.date.issued2021-10-04
dc.descriptionProceedings: 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems MASS 2021, 4-7 October 2021, Denver, CO, USA.en
dc.description.abstractThe Internet of Things (IoT) is connecting a massive scale of everyday objects to the internet. We need to ensure the secure connectivity and authentication of these devices. Physical (PHY)-layer identification methods can distinguish between different devices by leveraging their unique hardware imperfections. But these methods typically require large quantities of training data which makes them impractical for large deployment scenarios. Also, these methods do not address the PHY-layer identification of new devices joining an IoT network. In this paper, we propose a PHY-layer identification method using one-shot learning that can identify new devices using the network solicitation packet of the devices as reference packets. We show that our method can accurately identify new devices without training, achieving a precision and recall over 80% even in the presence of 10 dBm noise. Furthermore, we show that with minimal retraining using only three packets from each device, we can accurately identify all devices in the IoT network with a precision and recall of 93%.en
dc.description.sponsorshipThis work has been (partially) funded by the H2020 EU/TW joint action 5G-DIVE (Grant #859881), and VINNOVA.en
dc.format.extent9
dc.identifier.bibliographicCitationHazra, S., Voigt, T. & Yan, W. (4-7 October, 2021). PLIO: Physical Layer Identification using One-shot Learning [Proceedings]. 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), Denver, CO, USA.en
dc.identifier.doihttps://doi.org/10.1109/MASS52906.2021.00050
dc.identifier.isbn978-1-6654-4935-9
dc.identifier.publicationfirstpage335
dc.identifier.publicationlastpage343
dc.identifier.publicationtitle2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)en
dc.identifier.urihttps://hdl.handle.net/10016/33904
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate2021-10-04
dc.relation.eventplaceUSAen
dc.relation.eventtitle2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/GA-859881
dc.rights© 2021, IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherRF fingerprintingen
dc.subject.otherSecurityen
dc.subject.otherDeep-learningen
dc.titlePLIO: Physical Layer Identification using One-shot Learningen
dc.typeconference proceedings*
dc.type.hasVersionAM*
dspace.entity.typePublication
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