Node-wise Hardware Trojan Detection Based on Graph Learning

Abstract

In the fourth industrial revolution, securing the protection of supply chains has become an ever-growing concern. One such cyber threat is a hardware Trojan (HT), a malicious modification to an IC. HTs are often identified during the hardware manufacturing process but should be removed earlier in the design process. Machine learning-based HT detection in gate-level netlists is an efficient approach to identifying HTs at the early stage. However, feature-based modeling has limitations in terms of discovering an appropriate set of HT features. We thus propose NHTD-GL in this paper, a novel node-wise HT detection method based on graph learning (GL). Given the formal analysis of the HT features obtained from domain knowledge, NHTD-GL bridges the gap between graph representation learning and feature-based HT detection. The experimental results demonstrate that NHTD-GL achieves 0.998 detection accuracy and 0.921 F1-score and outperforms state-of-the-art node-wise HT detection methods. NHTD-GL extracts HT features without heuristic feature engineering.