DART: Distribution-Aware Hardware Trojan Detection

Machine Learning (ML) has proven effective in Integrated Circuits (IC) security, particularly in Hardware Trojan (HT) detection. However, a model’s generalization potential depends on its ability to address distribution shifts (DS) in unseen data. Mitigating DS enhances a model’s adaptability to novel variations and threats within the dynamic realm of IC designs and HTs. We formulate HT detection as a DS problem, introducing DART, a novel Distribution-Aware HT detection framework, to enhance model generalization. Applying DART on state-of-the-art Graph Neural Network architecture yields up to 22.96% and 17.37% F1-score improvements for unseen IC designs diverging significantly from the training data.