Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/71467

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

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Title:Multi-subcarrier Physical Layer Authentication Using Channel State Information and Deep Learning
Authors:St. Germain, Ken
Kragh, Frank
Keywords:Cyber Systems: Their Science, Engineering, and Security
authentication
csi
deep learning
gan
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Date Issued:05 Jan 2021
Abstract:Strong 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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/71467
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.846
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Cyber Systems: Their Science, Engineering, and Security


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