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

TL;DR

TrojanSAINT introduces a graph neural network-based method for detecting and localizing hardware Trojans at the gate level, achieving high accuracy in both pre- and post-silicon scenarios.

Contribution

It presents a novel sampling-based GNN framework for hardware Trojan detection and localization, outperforming prior methods and enabling practical validation.

Findings

Achieves up to 98% TPR and 96% TNR on TrustHub benchmarks.

Outperforms prior GNN-based and baseline classifiers.

Supports both pre- and post-silicon Trojan detection.

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.