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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    FPGA Hardware Trojan Detection: Golden-Free Machine Learning Approach


    Contributors:


    Publication date :

    2023-08-28


    Size :

    1518495 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Power analysis-based Hardware Trojan detection

    Xue, Hao / Li, Shuo / Ren, Saiyu | IEEE | 2017


    Hardware Trojan Detection using Xilinx Vivado

    Marlow, Ryan / Harper, Scott / Batchelor, Whitney et al. | IEEE | 2018


    Hardware trojan state detection for analog circuits and systems

    Wang, Yen-Ting / Wang, Qianqian / Chen, Degang et al. | IEEE | 2014


    Low overhead design for improving hardware trojan detection efficiency

    Xue, Hao / Moody, Tyler / Li, Shuo et al. | IEEE | 2014


    Enhancing FPGA security through Trojan resilient IP creation

    Hazari, Noor Ahmad / Niamat, Mohammed | IEEE | 2017