Towards Machine-Learning-based Oracle-Guided Analog Circuit Deobfuscation

Oracle-guided circuit deobfuscation/learning is the problem of recovering unknowns from a circuit by making input-output queries to it and it has various applications in the hardware security domain: in assessing the security of obfuscation schemes, side-channel analysis, and reverse engineering. Unlike the digital case, the generic analog version of the problem has received less attention with existing approaches requiring manual instance-specific labor. In this paper, we present a novel automated approach to this end: using machine learning models trained on synthetic data to predict unknown values from query data. We evaluate our framework approach in a proof-of-concept implementation against a set of hand-crafted diverse analog circuits from simple resistive networks to complex op-amp circuits, using a variety of machine learning models from linear models to decision trees, and (graph) neural networks. Our experiments show prediction error rates of less than 5% on these circuit sets. We explore additional questions such as the impact of uncertainty sampling, topological information, out-of-training range data, and parameter (process) variation.