Toward Precise and Explainable Hardware Trojan Localization at LUT Level
Hao Su, Wei Hu, Xuelin Zhang, Dan Zhu, Lingjuan Wu · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2025
Trojans represent a severe threat to hardware security and trust. This work investigates the Trojan detection problem from a unique viewpoint and proposes a novel hardware Trojan localization method targeting FPGA netlists. The proposed method automatically extracts the rich structural and behavioral features at look-up-table (LUT) level to train an explainable graph neural network (GNN) model for classifying design nodes in FPGA netlists and identifying the Trojan-infected ones. Experimental results using 183 hardware Trojan benchmarks show that our method successfully pinpoints Trojan-infected nodes with true positive rate, accuracy and area under the ROC curve (AUC) of 95.14%, 95.71% and 95.46% respectively. To the best of our knowledge, this is the first LUT level Trojan localization solution using explainable GNNs.