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

    22 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 Sep 25
    Event50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
    Duration: 2017 May 282017 May 31

    Other

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

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    Keywords

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

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

    Cite this

    Hasegawa, K., Yanagisawa, M., & Togawa, N. (2017). Trojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier. In IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings [8050827] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2017.8050827