TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection
Hazem Lashen, Lilas Alrahis, Johann Knechtel, Ozgur Sinanoglu
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.
Topics & Concepts
NetlistHardware TrojanComputer scienceTrojanScheme (mathematics)Artificial intelligenceBaseline (sea)Artificial neural networkEmbedded systemPattern recognition (psychology)MathematicsMathematical analysisOceanographyComputer securityGeologyPhysical Unclonable Functions (PUFs) and Hardware SecurityIntegrated Circuits and Semiconductor Failure AnalysisNeuroscience and Neural Engineering