Accurate Hardware Trojan Detection Technology Based on Node Concealment Features

The globalization of the IC supply chain has increased the possibility for adversaries to insert hardware Trojans (HTs), which are always inserted into nodes with high concealment. Although several methods have been introduced to detect HTs, they do not specifically focus on node concealment and are not accurate enough. In this paper, 11 new features are proposed reflecting the node concealment, i.e, the controllable difficulty and observable difficulty of nodes, for machine-learning-based HT detection. The proposed features comprehensively consider the circuit structure information and signal propagation characteristics, and quantify the possibility of attacks. After combining the proposed features with existing ones for classification, the true positive rate (TPR) is 35 points higher than that without using the proposed features. Compared with the state-of-the-art works, our TPR and true negative rate (TNR) have increased by 15.6% and 2.1%, respectively, especially in large-scale circuits. It shows that our method has a great advantage in detection accuracy.