Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods

Abstract

The proliferation of the Internet of Things (IoT) has created significant opportunities for future telecommunications. A popular category of IoT devices is oriented toward low-cost and low-power applications. However, certain aspects of such category, including the authentication process, remain inadequately investigated against cyber vulnerabilities. This is caused by the inherent trade-off between device complexity and security rigor. In this work, we propose an authentication method based on radio frequency fingerprinting (RFF) using deep learning. This method can be implemented on the base station side without increasing the complexity of the IoT devices. Specifically, we propose four representation modalities based on continuous wavelet transform (CWT) to exploit tempo-spectral radio fingerprints. Accordingly, we utilize the generative adversarial network (GAN) and convolutional neural network (CNN) for spoof detection and authentication. For empirical validation, we consider the widely popular LoRa system with a focus on the preamble of the radio frame. The presented experimental test involves 20 off-the-shelf LoRa modules to demonstrate the feasibility of the proposed approach, showing reliable detection results of spoofing devices and high-level accuracy in authentication of 92.4%.