• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

A Study of Machine Learning based Hardware Trojans Detection Mechanisms for FPGAs


Jaedong Jang, Mingi Cho, Yezee Seo, Seyeon Jeong, Taekyoung Kwon, Journal of Internet Computing and Services, Vol. 21, No. 2, pp. 109-119, Apr. 2020
10.7472/jksii.2020.21.2.109, Full Text:
Keywords: FPGA, Hardware Security, Hardware Trojan

Abstract

The FPGAs are semiconductors that can be redesigned after initial fabrication. It is used in various embedded systems such as signal processing, automotive industry, defense and military systems. However, as the complexity of hardware design increases and the design and manufacturing process globalizes, there is a growing concern about hardware trojan inserted into hardware. Many detection methods have been proposed to mitigate this threat. However, existing methods are mostly targeted at IC chips, therefore it is difficult to apply to FPGAs that have different components from IC chips, and there are few detection studies targeting FPGA chips. In this paper, we propose a method to detect hardware trojan by learning the static features of hardware trojan in LUT-level netlist of FPGA using machine learning.


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Cite this article
[APA Style]
Jaedong Jang, Mingi Cho, Yezee Seo, Seyeon Jeong, & Taekyoung Kwon (2020). A Study of Machine Learning based Hardware Trojans Detection Mechanisms for FPGAs. Journal of Internet Computing and Services, 21(2), 109-119. DOI: 10.7472/jksii.2020.21.2.109.

[IEEE Style]
J. Jang, M. Cho, Y. Seo, S. Jeong and T. Kwon, "A Study of Machine Learning based Hardware Trojans Detection Mechanisms for FPGAs," Journal of Internet Computing and Services, vol. 21, no. 2, pp. 109-119, 2020. DOI: 10.7472/jksii.2020.21.2.109.

[ACM Style]
Jaedong Jang, Mingi Cho, Yezee Seo, Seyeon Jeong, and Taekyoung Kwon. 2020. A Study of Machine Learning based Hardware Trojans Detection Mechanisms for FPGAs. Journal of Internet Computing and Services, 21, 2, (2020), 109-119. DOI: 10.7472/jksii.2020.21.2.109.