The growing dependence on third-party foundries for integrated circuit (IC) fabrication has created major security concerns because of hardware Trojan (HT) insertion risks. Traditional detection methods, including side-channel analysis and golden reference models, face limitations such as sensitivity to noise, high cost, and impracticality for large-scale deployment. This work introduces a machine learning framework for HT detection that eliminates the need for golden references. The framework automatically extracts statistical features from chip data, groups chips into clusters, and uses an internal filtering process to identify the most reliable patterns. These patterns are then used to guide a learning model that can accurately separate Trojan-infected chips from clean ones. Experimental evaluation demonstrates that the proposed method achieves high detection accuracy with zero false negatives, while remaining resilient against adversarial perturbations. These findings indicate that cluster-filtered pseudo-labeling provides a practical and scalable solution for enhancing hardware security in modern IC supply chains.
Ghimire et al. (Thu,) studied this question.