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

Tomotaka Inoue, Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa

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

    3 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017
    PublisherIEEE Computer Society
    Pages811-814
    Number of pages4
    Volume2017-October
    ISBN (Electronic)9781509066247
    DOIs
    Publication statusPublished - 2018 Jan 8
    Event12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017 - Guiyang, China
    Duration: 2017 Oct 252017 Oct 28

    Other

    Other12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017
    CountryChina
    CityGuiyang
    Period17/10/2517/10/28

    Fingerprint

    Support vector machines
    Hardware
    Learning systems
    Classifiers
    Transceivers
    Hardware security
    Networks (circuits)

    Keywords

    • Design time
    • Gate-level netlist
    • Hardware Trojan
    • Machine learning
    • Support vector machine

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Electrical and Electronic Engineering

    Cite this

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

    Designing hardware trojans and their detection based on a SVM-based approach. / Inoue, Tomotaka; Hasegawa, Kento; Yanagisawa, Masao; Togawa, Nozomu.

    Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017. Vol. 2017-October IEEE Computer Society, 2018. p. 811-814.

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

    Inoue, T, Hasegawa, K, Yanagisawa, M & Togawa, N 2018, Designing hardware trojans and their detection based on a SVM-based approach. in Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017. vol. 2017-October, IEEE Computer Society, pp. 811-814, 12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017, Guiyang, China, 17/10/25. https://doi.org/10.1109/ASICON.2017.8252600
    Inoue T, Hasegawa K, Yanagisawa M, Togawa N. Designing hardware trojans and their detection based on a SVM-based approach. In Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017. Vol. 2017-October. IEEE Computer Society. 2018. p. 811-814 https://doi.org/10.1109/ASICON.2017.8252600
    Inoue, Tomotaka ; Hasegawa, Kento ; Yanagisawa, Masao ; Togawa, Nozomu. / Designing hardware trojans and their detection based on a SVM-based approach. Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017. Vol. 2017-October IEEE Computer Society, 2018. pp. 811-814
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