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\begin{document}

\title{An Investigation into the Impacts of Deep Learning-based Re-sampling on
Specific Emitter Identification Performance}



\author[1]{Mohamed Fadul}%
\author[2]{Donald Reising}%
\author[1]{Lakmali Weerasena}%
\affil[1]{The University of Tennessee at Chattanooga}%
\affil[2]{The University of Tennessee at Chattanooga College of Engineering and Computer Science}%


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  \date{May 04, 2024}


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\begin{abstract}
Increasing Internet of Things (IoT) deployments present a growing
surface over which villainous actors can carry out attacks. This
disturbing revelation is amplified by the fact that a majority of IoT
devices use weak or no encryption at all. Specific Emitter
Identification (SEI) is an approach intended to address this IoT
security weakness. This work provides the first Deep Learning (DL)
driven SEI approach that upsamples the signals after collection to
improve performance while simultaneously reducing the hardware
requirements of the IoT devices that collect them. DL-driven upsampling
results in superior SEI performance versus two traditional upsampling
approaches and a convolutional neural network only approach.%
\end{abstract}%



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