Classification of Electronic Devices and Software Processes via Unintentional Electronic Emissions With Neural Decoding Algorithms

Abstract

Electronic and electromechanical devices continuously emit electromagnetic (EM) signals while in use. These EM emissions (EMEs) contain unique spectral characteristics that can be leveraged for a variety of purposes, including identification of the device's unique EM “fingerprint,” and characterization of software processes running on the device. In this study, we implemented a novel method for automatic identification and characterization of these EMEs inspired by a classification/decoding scheme used to extract neural correlates of brain state from magnetoencephalographic data. Utilizing a sparse bilinear formulation of logistic regression as our “neural” decoder, we extracted device- and software-specific spectrospatial patterns from five identical Arduino Uno prototyping boards as they cycled through five program states. In the device fingerprinting task, we were able to discriminate all five boards from each other with near-perfect accuracy using this method. For software characterization, within a single device, we detected all five programs with 90% accuracy, and across devices, we were able to identify 3/5 programs with 99% accuracy, and achieving 77% accuracy for the other two. Overall, this neural-decoding approach performed well in all scenarios tested, and the corresponding EME “maps” it provides quantify the differences between device and software-specific EMEs in an easily interpretable way.