TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection

Abstract

We propose TrojanSAINT, a graph neural network (GNN)-based hardware Trojan (HT) detection scheme working at the gate level. Unlike prior GNN-based art, TrojanSAINT enables both pre-/post-silicon HT detection. TrojanSAINT leverages a sampling-based GNN framework to detect and also localize HTs. For practical validation, TrojanSAINT achieves on average (oa) 78% true positive rate (TPR) and 85% true negative rate (TNR), respectively, on various TrustHub HT benchmarks. For best-case validation, TrojanSAINT even achieves 98% TPR and 96% TNR oa. TrojanSAINT outperforms related prior works and baseline classifiers. We release our source codes and result artifacts.

Will be presented at the IEEE International Symposium on Circuits and Systems (ISCAS), 2023