An investigation into the impacts of deep learning-based re-sampling on specific emitter identification performance

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 most IoT devices use weak or no encryption. 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 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. This work provides the first Deep Learning (DL) driven Specific Emitter Identification (SEI) approach that upsamples the signals after collection to improve performance while simultaneously reducing the hardware requirements of the IoT devices that collect them.image