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

Hazem Lashen; Lilas Alrahis; Johann Knechtel; Ozgur Sinanoglu

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arXiv · 2301.11804

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

Hazem Lashen·Lilas Alrahis·Johann Knechtel·Ozgur Sinanoglu

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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.

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BibTeX RIS

Hazem Lashen, Lilas Alrahis, Johann Knechtel, Ozgur Sinanoglu. 2023-01-27. TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection. https://arxiv.org/abs/2301.11804

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