Multi-subcarrier Physical Layer Authentication Using Channel State Information and Deep Learning

Simple item page
dc.contributor.authorSt. Germain, Ken
dc.contributor.authorKragh, Frank
dc.date.accessioned2020-12-24T20:28:06Z
dc.date.available2020-12-24T20:28:06Z
dc.date.issued2021-01-05
dc.description.abstractStrong authentication is crucial as wireless networks become more widespread and relied upon. The robust physical layer features produced by advanced communication networks lend themselves to accomplishing physical layer authentication by using channel state information (CSI). The use of deep learning with neural networks is well suited for classification tasks and can further the goal of enhancing physical layer security. To that end, we propose a semi-supervised generative adversarial network to differentiate between legitimate and malicious transmitters and accurately identify devices for authentication across a range of signal to noise ratio conditions. Our system leverages multiple input multiple output CSI across orthogonal frequency division multiplexing subcarriers using a small percentage of labeled training data.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.846
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/71467
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCyber Systems: Their Science, Engineering, and Security
dc.subjectauthentication
dc.subjectcsi
dc.subjectdeep learning
dc.subjectgan
dc.subjectmimo
dc.titleMulti-subcarrier Physical Layer Authentication Using Channel State Information and Deep Learning
prism.startingpage7036

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0688.pdf
Size:
1.07 MB
Format:
Adobe Portable Document Format
Download

Collections

Cyber Systems: Their Science, Engineering, and Security