Trojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier

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

129 Citations (Scopus)

Abstract

Recently, due to the increase of outsourcing in IC design, it has been reported that malicious third-party vendors often insert hardware Trojans into their ICs. How to detect them is a strong concern in IC design process. The features of hardware-Trojan infected nets (or Trojan nets) in ICs often differ from those of normal nets. To classify all the nets in netlists designed by third-party vendors into Trojan ones and normal ones, we have to extract effective Trojan features from Trojan nets. In this paper, we first propose 51 Trojan features which describe Trojan nets from netlists. Based on the importance values obtained from the random forest classifier, we extract the best set of 11 Trojan features out of the 51 features which can effectively detect Trojan nets, maximizing the F-measures. By using the 11 Trojan features extracted, the machine-learning based hardware Trojan classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several TrustHUB benchmarks and obtained the average F-measure of 74.6%, which realizes the best values among existing machine-learning-based hardware-Trojan detection methods.

Original languageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems
Subtitle of host publicationFrom Dreams to Innovation, ISCAS 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467368520
DOIs
Publication statusPublished - 2017 Sept 25
Event50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
Duration: 2017 May 282017 May 31

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Other

Other50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Country/TerritoryUnited States
CityBaltimore
Period17/5/2817/5/31

Keywords

  • F-measure
  • gate-level netlist
  • hardware Trojan
  • machine learning
  • random forest

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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