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arXiv · 2305.03853

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

Mohamed K. M. Fadul·Donald R. Reising·Lakmali P. Weerasena
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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.

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BibTeXRIS

Mohamed K. M. Fadul, Donald R. Reising, Lakmali P. Weerasena. 2023-05-05. An Investigation into the Impacts of Deep Learning-based Re-sampling on Specific Emitter Identification Performance. https://arxiv.org/abs/2305.03853

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