Feature Learning for Enhanced Security in the Internet of Things Identifying Internet of Things (IoT) devices by their Radio Frequency (RF) fingerprint has important security implications. As the number of connected devices grows, current authentication mechanisms are becoming more susceptible to device spoofing attacks. To combat this, we exploit hardware imperfections in the RF transmit chain to extract devicespecific features that uniquely identify an emitter, providing an additional layer of security. This is accomplished with a complexvalued Variational Autoencoder that has a Gaussian Mixture (GMVAE) prior on the latent variablesxe2x80x99 marginal distribution. By exploiting sequential information in the RF timeseries data, we achieve processing gain by integrating multiple latentspace representations from a single device. We test and analyze the proposed approach on real WiFi data and obtain excellent classification results. We also test the proposed model on an OutofDistribution (OOD) detection task.

A New RFPUF Based Authentication of Internet of Things Using Random Forest Classification This paper presents a novel RFPUF based authentication framework which exploits the intrinsic nonidealities in physical characteristic of a devicemedium to generate a unique identity for wireless nodes. It also takes the advantage of Random Forest classification to securely identify the sender nodes based on their unique features extracting from alreadyexisting modules in the receiver side. In contrast to the neural networkbased schemes, our proposed approach incurs lower design complexity and overheads, while it no longer needs a large amount of preparatory and preprocessing works related to the learning process and adjusting the network parameters. Thus, the overall runtime required to preparing and testing of network is drastically lessened. The experimental results show that the proposed scheme can reach to 100% accuracy in the identification of 225 nodes when a forest network with 100 trees and depth of 20 is developed, posing a negligible overhead on the receiver side. This high accuracy can be nearly achieved even in the presence of channel variations as our approach has less sensitivity to environmental conditions.