• Microelectronics
  • Vol. 53, Issue 1, 164 (2023)
CHEN Jiawei1, LIU Hongjin2, ZHANG Shaolin2, LI Bin2..., LI Kang1, WEN Cong1, ZHOU You2, PAN Weitao3 and SHI Jiangyi1|Show fewer author(s)
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  • 1[in Chinese]
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    DOI: 10.13911/j.cnki.1004-3365.210491 Cite this Article
    CHEN Jiawei, LIU Hongjin, ZHANG Shaolin, LI Bin, LI Kang, WEN Cong, ZHOU You, PAN Weitao, SHI Jiangyi. A Hardware Trojan Detection Method Based on Cascade Structure[J]. Microelectronics, 2023, 53(1): 164 Copy Citation Text show less
    References

    [1] TEHRANIPOOR M, SALMANI H, ZHANG X, et al. Trustworthy hardware: trojan detection and design-for-trust challenges [J]. IEEE Comput Magaz, 2011, 44(7): 66-74.

    [2] SHAKYA B, HE T, SALMANI H, et al. Benchmarking of hardware trojans and maliciously affected circuits [J] J Hardware Syst Secur, 2017, 1(1): 85-102.

    [3] LIU Q, ZHAO P, CHEN F. A hardware trojan detection method based on structural features of trojan and host circuits [J]. IEEE Access, 2019, 7: 44632-44644.

    [4] HICKS M, FINNICUM M, KING S T, et al. Overcoming and untrusted computing base: detecting and removing malicious hardware automatically [C] // 31st IEEE Symp Secur Privacy. Oakland, CA, USA. 2010: 159-172.

    [5] WAKSMAN A, SUOZZO M, SETHUMADHAVAN S. FANCI: identification of stealthy malicious logic using boolean functional analysis [C] // The 2013 ACM SIGSAC Conf Comput & Commun Secur. Berlin, Germany. 2013: 697-708.

    [6] OYA M, SHI Y, YANAGISAWA M, et al. A score-based classification method for identifying hardware-trojans at gate-level netlists [C] // Design, Automation & Test Europe Conf & Exhib (DATE). Grenoble, France. 2015: 465-470.

    [7] HASEGAWA K, OYA M, YANAGISAWA M, et al. Hardware trojans classification for gate-level netlists based on machine learning [C] // IEEE 22nd Int Symp On-Line Testing Robust Syst Design (IOLTS). Catalunya, Spain. 2016: 203-206.

    [8] HASEGAWA K, YANAGISAWA M, TOGAWA N. Trojan-feature extraction at gate-level netlists and its application to hardware-trojan detection using random forest classifier [C] // IEEE Int Symp Circ Syst (ISCAS). Baltimore, MD, USA. 2017: 1-4.

    [9] SALMANI H. COTD: reference-free hardware trojan detection and recovery based on controllability and observability in gate-level netlist [J]. IEEE Trans Inform Forensics Secur, 2017, 12(2): 338-350.

    [10] SHARMA R, VALIVATI N K, SHARMA G K, et al. A new hardware trojan detection technique using class weighted XGBoost classifier [C] // 24th Int Symp VLSI Design Test (VDAT). Bhubaneswar, India. 2020: 1-6.

    [11] LU R, SHEN H, SU Y, et al. GramsDet: hardware trojan detection based on recurrent neural network [C] // IEEE 28th Asian Test Symp (ATS). Kolkata, India. 2019: 111-115.

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    CHEN Jiawei, LIU Hongjin, ZHANG Shaolin, LI Bin, LI Kang, WEN Cong, ZHOU You, PAN Weitao, SHI Jiangyi. A Hardware Trojan Detection Method Based on Cascade Structure[J]. Microelectronics, 2023, 53(1): 164
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