  In the fourth industrial revolution, securing the protection of the supply
chain 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 in the
hardware manufacturing process, but should be removed earlier, when the design
is being specified. Machine learning-based HT detection in gate-level netlists
is an efficient approach to identify HTs at the early stage. However,
feature-based modeling has limitations in 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 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
outperforms state-of-the-art node-wise HT detection methods. NHTD-GL extracts
HT features without heuristic feature engineering.
