DART: Distribution-Aware Hardware Trojan Detection
Luke Chen, Youssef Gamal, Yanda Li, Shih-Yuan Yu, Ihsen Alouani, Mohammad Abdullah Al Faruque · IEEE Transactions on Information Forensics and Security · 2025
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, introducingDART, a novelDistribution-AwareHT detection framework, to enhance model generalization. ApplyingDARTon 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.