Designing hardware trojans and their detection based on a SVM-based approach

Tomotaka Inoue, Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa

    研究成果: Conference contribution

    9 引用 (Scopus)

    抜粋

    Since hardware production become inexpensive and international, hardware vendors often outsource their products to third-party vendors. Due to the situation, malicious vendors can easily insert malfunctions (also known as 'hardware Trojans') to their products. In this paper, we experimentally evaluate a machine-learning-based hardware-Trojan detection method using several hardware Trojans we designed. To begin with, we design three types of hardware Trojans and insert them to simple RS232 transceiver circuits. After that, we learn known netlists, where we know which nets are Trojan ones or normal ones beforehand, using a machine-learning-based hardware-Trojan detection method with a support vector machine (SVM) classifier. Finally, we classify the nets in the designed hardware-Trojan-inserted netlists into a set of Trojan nets and that of normal nets using the learned classifier. The experimental results demonstrate that the hardware-Trojan detection method with the SVM-based approach can detect a part of hardware Trojans we designed.

    元の言語English
    ホスト出版物のタイトルProceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017
    出版者IEEE Computer Society
    ページ811-814
    ページ数4
    2017-October
    ISBN(電子版)9781509066247
    DOI
    出版物ステータスPublished - 2018 1 8
    イベント12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017 - Guiyang, China
    継続期間: 2017 10 252017 10 28

    Other

    Other12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017
    China
    Guiyang
    期間17/10/2517/10/28

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Electrical and Electronic Engineering

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  • これを引用

    Inoue, T., Hasegawa, K., Yanagisawa, M., & Togawa, N. (2018). Designing hardware trojans and their detection based on a SVM-based approach. : Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017 (巻 2017-October, pp. 811-814). IEEE Computer Society. https://doi.org/10.1109/ASICON.2017.8252600