Data Augmentation for Machine Learning-Based Hardware Trojan Detection at Gate-Level Netlists

Kento Hasegawa, Seira Hidano, Kohei Nozawa, Shinsaku Kiyomoto, Nozomu Togawa

研究成果: Conference contribution

抄録

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).

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665433709
DOI
出版ステータスPublished - 2021 6 28
イベント27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021 - Virtual, Online
継続期間: 2021 6 282021 6 30

出版物シリーズ

名前Proceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021

Conference

Conference27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
CityVirtual, Online
Period21/6/2821/6/30

ASJC Scopus subject areas

  • ソフトウェア
  • エネルギー工学および電力技術
  • 電子工学および電気工学
  • 安全性、リスク、信頼性、品質管理
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ

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