@inproceedings{a784c43c50e5474db908584f1c1def9a,
title = "Data Augmentation for Machine Learning-Based Hardware Trojan Detection at Gate-Level Netlists",
abstract = "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). ",
keywords = "data augmentation, gate-level, hardware Trojan, machine learning, netlist",
author = "Kento Hasegawa and Seira Hidano and Kohei Nozawa and Shinsaku Kiyomoto and Nozomu Togawa",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021 ; Conference date: 28-06-2021 Through 30-06-2021",
year = "2021",
month = jun,
day = "28",
doi = "10.1109/IOLTS52814.2021.9486713",
language = "English",
series = "Proceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021",
}