Hardware Trojans classification for gate-level netlists using multi-layer neural networks

    研究成果: Conference contribution

    25 引用 (Scopus)

    抜粋

    Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.

    元の言語English
    ホスト出版物のタイトル2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ227-232
    ページ数6
    ISBN(電子版)9781538603512
    DOI
    出版物ステータスPublished - 2017 9 19
    イベント23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 - Thessaloniki, Greece
    継続期間: 2017 7 32017 7 5

    Other

    Other23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
    Greece
    Thessaloniki
    期間17/7/317/7/5

      フィンガープリント

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Safety, Risk, Reliability and Quality
    • Computer Networks and Communications
    • Hardware and Architecture
    • Control and Systems Engineering

    これを引用

    Hasegawa, K., Yanagisawa, M., & Togawa, N. (2017). Hardware Trojans classification for gate-level netlists using multi-layer neural networks. : 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 (pp. 227-232). [8046227] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IOLTS.2017.8046227