Hardware Trojan Detection Using Shapley Ensemble Boosting

Abstract

Due to globalized semiconductor supply chain, there is an increasing risk of exposing system-on-chip designs to hardware Trojans (HT). While there are promising machine Learning based HT detection techniques, they have three major limitations: ad-hoc feature selection, lack of explainability, and vulnerability towards adversarial attacks. In this paper, we propose a novel HT detection approach using an effective combination of Shapley value analysis and boosting framework. Specifically, this paper makes two important contributions. We use Shapley value (SHAP) to analyze the importance ranking of input features. It not only provides explainable interpretation for HT detection, but also serves as a guideline for feature selection. We utilize boosting (ensemble learning) to generate a sequence of lightweight models that significantly reduces the training time while provides robustness against adversarial attacks. Experimental results demonstrate that our approach can drastically improve both detection accuracy (up to 24.6%) and time efficiency (up to 5.1x) compared to state-of-the-art HT detection techniques.