A Hardware-Trojan Classification Method Using Machine Learning at Gate-Level Netlists Based on Trojan Features-Reference-Cited by-同舟云学术

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A Hardware-Trojan Classification Method Using Machine Learning at Gate-Level Netlists Based on Trojan Features

Published:2017 Issue:7 Volume:E100.A Page:1427-1438
ISSN:0916-8508
Container-title:IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
language:en
Short-container-title:IEICE Trans. Fundamentals

Author:

HASEGAWA Kento¹,YANAGISAWA Masao¹,TOGAWA Nozomu¹

Affiliation:

1. Dept. of Computer Science and Communications Engineering, Waseda University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing

Link

https://www.jstage.jst.go.jp/article/transfun/E100.A/7/E100.A_1427/_pdf

Reference21 articles.

1. [1] B. Cakir and S. Malik, “Hardware Trojan detection for gate-level ICs using signal correlation based clustering,” Proc. Design, Automation and Test in Europe (DATE), pp.471-476, 2015. 10.7873/date.2015.0365

2. [2] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learn., vol.20, no.3, pp.273-297, 1995. 10.1007/bf00994018

3. [3] S. Dreiseitl and L. Ohno-Machado, “Logistic regression and artificial neural network classification models: A methodology review,” J. Biomed. Inform., vol.35, no.5-6, pp.352-359, 2002. 10.1016/s1532-0464(03)00034-0

4. [4] J. Francq and F. Frick, “Introduction to hardware Trojan detection methods,” Proc. Design, Automation and Test in Europe (DATE), pp.770-775, 2015. 10.7873/date.2015.1101

5. [5] C. Goutte and E. Gaussier, “A probabilistic interpretation of precision, recall and f-score, with implication for evaluation,” 27th European Conference on IR Research, ECIR 2005, vol.3408, pp.345-359, 2005. 10.1007/978-3-540-31865-1_25

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