Structural and SCOAP Features Based Approach for Hardware Trojan Detection Using SHAP and Light Gradient Boosting Model

Abstract

Hardware Trojan (HT) is the most critical threat due to outsourcing of Integrated circuit designing phases. Therefore, a new technique is proposed that utilizes structural and SCOAP features to detect HT from the gate-level netlist using Light Gradient Boosting (Light GBM). Further, a model agnostic Shapley additive explanations (SHAP) is employed to identify each feature global and local impact on model prediction. Moreover, a quartile-based feature selection method is proposed, which uses SHAP to identify the optimal feature set by keeping low retraining rounds. Experimental results show that the proposed technique accurately detects always-on-Trojans and HT nets from Trust-Hub, DeTrust, and DeTest benchmarks.