PCB Identification Based on Machine Learning Utilizing Power Consumption Variability

Abstract

Manufacturing variability demonstrates significant variations in dynamic power consumption profiles during program execution, even if the printed circuit boards (PCB) are identical and the processors execute the same operations on the same data. In this work, we show how this variability can be leveraged to the benefit of manufacturers by utilizing machine learning (ML) based PCB identification. The proposed technique based on power consumption variability achieves 100% accuracy in identifying PCBs from their power consumption traces after training a linear discriminant analysis (LDA) classifier on a collection of 30 identical PCBs for two test sets collected several months apart.