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

2023/05/05 by Mohamed K. M. Fadul, Fadul, Mohamed K. M., Donald R. Reising +3
Computer Science · #Digital Media Forensic Detection #FOS: Electrical engineering #Physical Unclonable Functions (PUFs) and Hardware Security #Signal Processing (eess.SP) #Wireless Signal Modulation Classification #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2305.03853

openalex publication_date 2023/05/05 · openalex created_date 2023/05/10 · openalex updated_date 2026/07/28

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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