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arXiv · 2112.02213

Node-wise Hardware Trojan Detection Based on Graph Learning

Kento Hasegawa·Kazuki Yamashita·Seira Hidano·Kazuhide Fukushima·Kazuo Hashimoto·Nozomu Togawa
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Abstract

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.

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Kento Hasegawa, Kazuki Yamashita, Seira Hidano, Kazuhide Fukushima, Kazuo Hashimoto, Nozomu Togawa. 2022-03-16. Node-wise Hardware Trojan Detection Based on Graph Learning. https://arxiv.org/abs/2112.02213

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