Node-Wise Hardware Trojan Detection Based on Graph Learning
Kento Hasegawa, K. Yamashita, Seira Hidano, Kazuhide Fukushima, Kazuo Hashimoto, Nozomu Togawa
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 <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NHTD-GL</monospace> 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, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NHTD-GL</monospace> bridges the gap between graph representation learning and feature-based HT detection. The experimental results demonstrate that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NHTD-GL</monospace> achieves 0.998 detection accuracy and 0.921 F1-score and outperforms state-of-the-art node-wise HT detection methods. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NHTD-GL</monospace> extracts HT features without heuristic feature engineering.