Ensuring trust in the semiconductor IC supply chain necessitates the critical detection of Hardware Trojans, yet current methods relying on side-channel analysis often require the use of golden chips for verification. This research paper presents a novel approach to detect Hardware Trojans in the semiconductor IC supply chain, addressing the need for trust and eliminating the use of golden chips. By combining unsuper-vised machine learning and side-channel analysis, the proposed technique leverages unique features from on-chip ring-oscillator networks to identify anomalies through unsupervised clustering. Evaluation on FPGA chips with Trojan insertion demonstrated exceptional accuracy, surpassing alternative methods with a 99 % accuracy rate. The centroid-based clustering model exhibited superior performance with a slight edge in false positive rate and an fl score. This research contributes to enhancing trust in semiconductor IC supply chains by offering a fresh perspective on Hardware Trojan detection.


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    Titel :

    FPGA Hardware Trojan Detection: Golden-Free Machine Learning Approach


    Beteiligte:
    Ghimire, Ashutosh (Autor:in) / Amsaad, Fathi (Autor:in) / Hossain, Tanvir (Autor:in) / Hoque, Tamzidul (Autor:in) / Sherif, Ahmed (Autor:in)


    Erscheinungsdatum :

    2023-08-28


    Format / Umfang :

    1518495 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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