TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection. (arXiv:2301.11804v1 [cs.CR])

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 …

art benchmarks case detect detection framework hardware hts network neural network rate silicon trojan validation working