FP-GNN: A Graph Neural Network for Hardware Trojan Detection in Gate-Level Netlist

Abstract

Feature-based detection method has been widely used for hardware trojan detection where hardware trojan features are explicitly extracted and trained a classifier or build an algorithm that can differentiate hardware trojan signals from normal ones. This method shows a good performance in the available benchmark circuits. However, when tested on a randomly generated circuit, that contains different structural features of benchmark circuit, it started to fail detecting the stealthy signals, because of the constraints when the features are explicitly extracted. To overcome this situation, in this paper, the authors aim to explore the benefit of graph neural network (GNN) to enhance the hardware trojan detection performance in both benchmark circuits and randomly generated circuits. More specifically, finding the appropriate representation of the digital circuit that is suitable for the GNN model and that can learn hidden feature and relationship between the signals. Experiments show satisfactory result on benchmark circuit with an average accuracy of 95.34%. Further, tests done on randomly generated circuits gives positive result with an average accuracy of 90.82%.