Assessing the Presence of Intentional Waveform Structure In Preamble-based SEI

Abstract

Internet of Things (IoT) deployments continue at an accelerated rate and are projected to reach 75 billion by 2025. Most IoT devices employ weak or no encryption at all due to constrained onboard resources, prohibitive manufacturing costs, and challenges due to implementing and managing encryption at scale. Specific Emitter Identification (SEI) is an approach intended to address the security risks associated with unencrypted or weakly encrypted IoT deployments. SEI passively exploits inherent, unintentional waveform features that are imparted during normal device operations, thus it does not require modification of the device being identified. Recently, Deep Learning (DL)-based SEI has garnered a lot of interest due to its ability to learn discriminatory features directly from an emitter's digitally sampled waveforms, thus eliminating feature engineering commonly associated with SEI. DL-based SEI uses the entirety of the digitally sampled waveform, which includes the unintentional as well as intentional waveform structure. The intentional waveform structure does not convey emitter specific features, thus it is information not used by the DL-based SEI process. In fact, this work shows that the intentional waveform structure acts as a confuser that inhibits the DL network's ability to learn the features needed to discern one emitter from another and its negative impacts worsen as the number of emitters increases. Our work shows that frequency-domain removal of the intentional waveform structure coupled with a Long Short-Term Memory (LSTM) results in superior SEI performance.