Data Augmentation for Machine Learning-Based Hardware Trojan Detection at Gate-Level Netlists
Kento Hasegawa, Seira Hidano, Kohei Nozawa, Shinsaku Kiyomoto, Nozomu Togawa · 2021
Due to the rapid growth in the information and telecommunications industries, an untrusted vendor might compromise the complicated supply chain by inserting hardware Trojans (HTs). Although hardware Trojan detection methods at gate-level netlists employing machine learning have been developed, the training dataset is insufficient. In this paper, we propose a data augmentation method for machine-learning-based hardware Trojan detection. Our proposed method replaces a gate in a hardware Trojan circuit with logically equivalent gates. The experimental results demonstrate that our proposed method successfully enhances the classification performance with all the classifiers in terms of the true positive rates (TPRs).