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.
Cite
@article{arxiv.2301.11804,
title = {TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection},
author = {Hazem Lashen and Lilas Alrahis and Johann Knechtel and Ozgur Sinanoglu},
journal= {arXiv preprint arXiv:2301.11804},
year = {2023}
}
Comments
Will be presented at the IEEE International Symposium on Circuits and Systems (ISCAS), 2023