Designing subspecies of hardware trojans and their detection using neural network approach

Tomotaka Inoue, Kento Hasegawa, Yuki Kobayashi, Masao Yanagisawa, Nozomu Togawa

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

    Abstract

    Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as 'hardware Trojans') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.

    Original languageEnglish
    Title of host publication2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
    PublisherIEEE Computer Society
    Volume2018-September
    ISBN (Electronic)9781538660959
    DOIs
    Publication statusPublished - 2018 Dec 13
    Event8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 - Berlin, Germany
    Duration: 2018 Sep 22018 Sep 5

    Other

    Other8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
    CountryGermany
    CityBerlin
    Period18/9/218/9/5

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    Keywords

    • design time
    • gate-level netlist
    • hardware Trojan
    • machine learning
    • neural network

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Industrial and Manufacturing Engineering
    • Media Technology

    Cite this

    Inoue, T., Hasegawa, K., Kobayashi, Y., Yanagisawa, M., & Togawa, N. (2018). Designing subspecies of hardware trojans and their detection using neural network approach. In 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 (Vol. 2018-September). [8576247] IEEE Computer Society. https://doi.org/10.1109/ICCE-Berlin.2018.8576247