Physical Layer-Based IoT Security: An Investigation Into Improving Preamble-Based SEI Performance When Using Multiple Waveform Collections

Abstract

The Internet of Things (IoT) is a collection of inexpensive, semi-autonomous, Internet-connected devices that sense and interact within the physical world. IoT security is of paramount concern, because most IoT devices use weak or no encryption at all. This concern is exacerbated by the fact that the number of IoT deployments continues grow, IoT devices are being integrated into key infrastructures, and their weak or lack of encryption is being exploited. Specific Emitter Identification (SEI) is being investigated as an effective, cost-saving IoT security approach, because it is a passive technique that exploits inherent, distinct features that are unintentionally imparted to the waveform during its formation and transmission by the IoT device’s Radio Frequency (RF) front-end. Despite the amount of research conducted, SEI still faces roadblocks that hinder its integration within operational networks. Our work focuses on the lack of feature permanence across time and environments, which is designated herein as the “multi-day” problem. We present results and analysis for six distinct experiments focused on improving multi-day SEI performance through the use of multiple waveform representations, deeper Convolutional Neural Networks (CNNs), increasing numbers of waveforms, channel model impacts, and two channel mitigation techniques. Our work shows improved multi-day SEI performance using the waveform’s frequency-domain representation and a CNN comprised of four convolutional layers. However, the traditional channel model and both channel mitigation techniques fail to sufficiently mitigate or remove real-world channel impacts, which suggests that the channel may not be the dominant effect hindering multi-day SEI performance.