Hardware-Trojan Detection at Gate-level Netlists using a Gradient Boosting Decision Tree Model and its Extension using Trojan Probability Propagation

Technological devices have become deeply embedded in people's lives, and their demand is growing every year. It has been indicated that outsourcing the design manufacturing of integrated circuits, which are essential for technological devices, may lead to insertion malicious circuitry, called hardware Trojans (HTs). This paper proposes an HT detection method at gate-level netlists based on XGBoost, one best gradient boosting decision tree models. We first propose optimal set features among many feature candidates a netlist level through thorough evaluations. Then, we construct XGBoost-based with its optimized hyperparameters. Evaluation experiments were conducted from Trust-HUB benchmarks showed average F-measure 0.842 using proposed method. Also, newly Trojan probability propagation effectively corrects results apply it obtained by detection. improved 0.861. value 0.194 points higher than existing so far.