A unioned graph neural network based hardware Trojan node detection

Abstract

The globalization of the integrated circuit (IC) industry has raised concerns about hardware Trojans (HT), and there is an urgent need for efficient HT-detection methods of gate-level netlists. In this work, we propose an approach to detect Trojan-nodes at the gate level, based on graph learning. The proposed method does not require any golden model and can be easily integrated into the integrated circuits design flow. In addition, we further design a unioned GNN network to combine information from the input side, output side, and neighbor side of the directed graph to generate representative node embeddings. The experimental results show that it could achieve 93.4% in recall, 91.4% in F-measure, and 90.7% in precision on average across different designs, which outperforms the state-of-the-art HT detection methods.