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Register-Transfer-Level Features for Machine-Learning-Based Hardware Trojan Detection

Hau Sim CHOO, Chia Yee OOI, Michiko INOUE, Nordinah ISMAIL, Mehrdad MOGHBEL, Chee Hoo KOK

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Summary :

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.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.2 pp.502-509
Publication Date
2020/02/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.2019EAP1044
Type of Manuscript
PAPER
Category
VLSI Design Technology and CAD

Authors

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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