PLIO: Physical Layer Identification using One-shot Learning

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dc.contributor.author Hazra, Saptarshi
dc.contributor.author Voigt, Thiemo
dc.contributor.author Yan, Wenqing
dc.date.accessioned 2022-01-18T12:28:07Z
dc.date.available 2022-01-18T12:28:07Z
dc.date.issued 2021-10-04
dc.identifier.bibliographicCitation Hazra, 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.
dc.identifier.isbn 978-1-6654-4935-9
dc.identifier.uri http://hdl.handle.net/10016/33904
dc.description Proceedings: 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems MASS 2021, 4-7 October 2021, Denver, CO, USA.
dc.description.abstract The 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%.
dc.description.sponsorship This work has been (partially) funded by the H2020 EU/TW joint action 5G-DIVE (Grant #859881), and VINNOVA.
dc.format.extent 9
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2021, IEEE.
dc.subject.other RF fingerprinting
dc.subject.other Security
dc.subject.other Deep-learning
dc.title PLIO: Physical Layer Identification using One-shot Learning
dc.type conferenceObject
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/MASS52906.2021.00050
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/GA-859881
dc.type.version acceptedVersion
dc.relation.eventdate 2021-10-04
dc.relation.eventplace USA
dc.relation.eventtitle 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
dc.relation.eventtype proceeding
dc.identifier.publicationfirstpage 335
dc.identifier.publicationlastpage 343
dc.identifier.publicationtitle 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
dc.contributor.funder European Commission
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