TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection
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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 and 85 benchmarks. For best-case validation, TrojanSAINT even achieves 98 TNR oa. TrojanSAINT outperforms related prior works and baseline classifiers. We release our source codes and result artifacts.
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