Hardware Trojans classification for gate-level netlists based on machine learning

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

31 Citations (Scopus)

Abstract

Recently, we face a serious risk that malicious third-party vendors can very easily insert hardware Trojans into their IC products but it is very difficult to analyze huge and complex ICs. In this paper, we propose a hardware-Trojan classification method to identify hardware-Trojan infected nets (or Trojan nets) using a support vector machine (SVM). Firstly, we extract the five hardware-Trojan features in each net in a netlist. Secondly, since we cannot effectively give the simple and fixed threshold values to them to detect hardware Trojans, we represent them to be a five-dimensional vector and learn them by using SVM. Finally, we can successfully classify a set of all the nets in an unknown netlist into Trojan ones and normal ones based on the learned SVM classifier. We have applied our SVM-based hardware-Trojan classification method to Trust-HUB benchmarks and the results demonstrate that our method can much increase the true positive rate compared to the existing state-of-the-art results in most of the cases. In some cases, our method can achieve the true positive rate of 100%, which shows that all the Trojan nets in a netlist are completely detected by our method.

Original languageEnglish
Title of host publication2016 IEEE 22nd International Symposium on On-Line Testing and Robust System Design, IOLTS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages203-206
Number of pages4
ISBN (Electronic)9781509015061
DOIs
Publication statusPublished - 2016 Oct 20
Event22nd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2016 - Sant Feliu de Guixols, Catalunya, Spain
Duration: 2016 Jul 42016 Jul 6

Other

Other22nd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2016
CountrySpain
CitySant Feliu de Guixols, Catalunya
Period16/7/416/7/6

Keywords

  • gate-level netlist
  • hardware Trojan
  • machine learning
  • static detection
  • support vector machine (SVM)

ASJC Scopus subject areas

  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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  • Cite this

    Hasegawa, K., Oya, M., Yanagisawa, M., & Togawa, N. (2016). Hardware Trojans classification for gate-level netlists based on machine learning. In 2016 IEEE 22nd International Symposium on On-Line Testing and Robust System Design, IOLTS 2016 (pp. 203-206). [7604700] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IOLTS.2016.7604700