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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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).

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433709
DOIs
Publication statusPublished - 2021 Jun 28
Event27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021 - Virtual, Online
Duration: 2021 Jun 282021 Jun 30

Publication series

NameProceedings - 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

Keywords

  • data augmentation
  • gate-level
  • hardware Trojan
  • machine learning
  • netlist

ASJC Scopus subject areas

  • Software
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture

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