Autor: |
Hasegawa, Kento, Yamashita, Kazuki, Hidano, Seira, Fukushima, Kazuhide, Hashimoto, Kazuo, Togawa, Nozomu |
Rok vydání: |
2021 |
Předmět: |
|
Druh dokumentu: |
Working Paper |
Popis: |
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. |
Databáze: |
arXiv |
Externí odkaz: |
|