The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
As informações de nível de transferência de registro (RTL) dificilmente estão disponíveis para detecção de cavalos de Troia em hardware. Neste artigo, são propostas quatro características do Trojan RTL relacionadas à instrução de ramificação. A seleção de recursos de mínima redundância e máxima relevância (mRMR) é aplicada aos recursos de Trojan propostos para determinar as combinações de recursos recomendadas. As combinações de recursos são então testadas usando diferentes conceitos de aprendizado de máquina para determinar a melhor abordagem para classificar Trojans e ramificações normais. O resultado mostra que um algoritmo de classificação de árvore de decisão com todos os quatro recursos de Trojan propostos pode atingir uma taxa média de detecção positiva verdadeira de 93.72% em dados de teste não vistos.
Hau Sim CHOO
Universiti Teknologi Malaysia
Chia Yee OOI
Universiti Teknologi Malaysia
Michiko INOUE
Nara Institute of Science and Technology (NAIST)
Nordinah ISMAIL
Universiti Teknologi Malaysia
Mehrdad MOGHBEL
Universiti Teknologi Malaysia
Chee Hoo KOK
Universiti Teknologi Malaysia
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Hau Sim CHOO, Chia Yee OOI, Michiko INOUE, Nordinah ISMAIL, Mehrdad MOGHBEL, Chee Hoo KOK, "Register-Transfer-Level Features for Machine-Learning-Based Hardware Trojan Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 2, pp. 502-509, February 2020, doi: 10.1587/transfun.2019EAP1044.
Abstract: Register-transfer-level (RTL) information is hardly available for hardware Trojan detection. In this paper, four RTL Trojan features related to branching statement are proposed. The Minimum Redundancy Maximum Relevance (mRMR) feature selection is applied to the proposed Trojan features to determine the recommended feature combinations. The feature combinations are then tested using different machine learning concepts in order to determine the best approach for classifying Trojan and normal branches. The result shows that a Decision Tree classification algorithm with all the four proposed Trojan features can achieve an average true positive detection rate of 93.72% on unseen test data.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019EAP1044/_p
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@ARTICLE{e103-a_2_502,
author={Hau Sim CHOO, Chia Yee OOI, Michiko INOUE, Nordinah ISMAIL, Mehrdad MOGHBEL, Chee Hoo KOK, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Register-Transfer-Level Features for Machine-Learning-Based Hardware Trojan Detection},
year={2020},
volume={E103-A},
number={2},
pages={502-509},
abstract={Register-transfer-level (RTL) information is hardly available for hardware Trojan detection. In this paper, four RTL Trojan features related to branching statement are proposed. The Minimum Redundancy Maximum Relevance (mRMR) feature selection is applied to the proposed Trojan features to determine the recommended feature combinations. The feature combinations are then tested using different machine learning concepts in order to determine the best approach for classifying Trojan and normal branches. The result shows that a Decision Tree classification algorithm with all the four proposed Trojan features can achieve an average true positive detection rate of 93.72% on unseen test data.},
keywords={},
doi={10.1587/transfun.2019EAP1044},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - Register-Transfer-Level Features for Machine-Learning-Based Hardware Trojan Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 502
EP - 509
AU - Hau Sim CHOO
AU - Chia Yee OOI
AU - Michiko INOUE
AU - Nordinah ISMAIL
AU - Mehrdad MOGHBEL
AU - Chee Hoo KOK
PY - 2020
DO - 10.1587/transfun.2019EAP1044
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2020
AB - Register-transfer-level (RTL) information is hardly available for hardware Trojan detection. In this paper, four RTL Trojan features related to branching statement are proposed. The Minimum Redundancy Maximum Relevance (mRMR) feature selection is applied to the proposed Trojan features to determine the recommended feature combinations. The feature combinations are then tested using different machine learning concepts in order to determine the best approach for classifying Trojan and normal branches. The result shows that a Decision Tree classification algorithm with all the four proposed Trojan features can achieve an average true positive detection rate of 93.72% on unseen test data.
ER -