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

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Authors
St. Germain, Ken
Kragh, Frank
Subjects
Cyber Systems: Their Science, Engineering, and Security
authentication
csi
deep learning
gan
mimo
Advisors
Date of Issue
05 Jan 2021
Date
2021
Publisher
HICSS
Language
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.
Type
Conference Paper
Description
17 USC 105 interim-entered record; under temporary embargo.
Series/Report No
Department
Electrical and Computer Engineering (ECE)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
U.S. Government affiliation is unstated in article text.
Format
10 p.
Citation
St Germain, Ken, and Frank Kragh. "Multi-subcarrier Physical Layer Authentication Using Channel State Information and Deep Learning." Proceedings of the 54th Hawaii International Conference on System Sciences. 2021.
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