Node-wise Hardware Trojan Detection Based on Graph Learning
This addresses the need for early-stage hardware Trojan detection in integrated circuit design to enhance supply chain security, representing a domain-specific incremental improvement.
The paper tackles the problem of detecting hardware Trojans in gate-level netlists by proposing NHTD-GL, a node-wise detection method based on graph learning, which achieves 0.998 detection accuracy and outperforms state-of-the-art methods.
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.