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The number of Internet of Things (IoT) deployments is expected to reach 75.4billion by 2025. Roughly 70% of all IoT devices employ weak or no encryption;thus, putting them and their connected infrastructure at risk of attack bydevices that are wrongly authenticated or not authenticated at all. A physicallayer security approach -- known as Specific Emitter Identification (SEI) --has been proposed and is being pursued as a viable IoT security mechanism. SEIis advantageous because it is a passive technique that exploits inherent anddistinct features that are unintentionally added to the signal by the IoT RadioFrequency (RF) front-end. SEI's passive exploitation of unintentional signalfeatures removes any need to modify the IoT device, which makes it ideal forexisting and future IoT deployments. Despite the amount of SEI researchconducted, some challenges must be addressed to make SEI a viable IoT securityapproach. One challenge is the extraction of SEI features from signalscollected under multipath fading conditions. Multipath corrupts the inherentSEI features that are used to discriminate one IoT device from another; thus,degrading authentication performance and increasing the chance of attack. Thiswork presents two semi-supervised Deep Learning (DL) equalization approachesand compares their performance with the current state of the art. The twoapproaches are the Conditional Generative Adversarial Network (CGAN) and JointConvolutional Auto-Encoder and Convolutional Neural Network (JCAECNN). Bothapproaches learn the channel distribution to enable multipath correction whilesimultaneously preserving the SEI exploited features. CGAN and JCAECNNperformance is assessed using a Rayleigh fading channel under degrading SNR, upto thirty-two IoT devices, and two publicly available signal sets. The JCAECNNimproves SEI performance by 10% beyond that of the current state of the art.

Improving RF-DNA Fingerprinting Performance in an Indoor Multipath Environment Using Semi-Supervised Learning