A Correction Method Using Multiple Trained Models for Graph-Learning Based Hardware-Trojan Detection

In recent years, IoT (Internet of Things) devices have attracted much attention, and IC products have become widespread in our daily lives. With the increase in demand for ICs, third-party companies have intervened in the design and manufacturing phase of ICs, increasing the risk of malicious circuits called hardware Trojans (HTs) being inserted during these phases. As one of the methods to detect HT in the design phase, an HT detection method using graph learning for circuit design information has been proposed, and relatively high HT detection accuracy has been reported. In this paper, we propose a correction method using multiple trained graphlearning models to improve the accuracy of graph-learning based HT detection results. After applying the proposed correction method, the average F -score improve to 0.8861, while TPR and the precision improve to $89.24 \%$ and $93.31 \%$, respectively.