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  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E103-A   No.2   pp.502-509
Publication Date: 2020/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.2019EAP1044
Type of Manuscript: PAPER
Category: VLSI Design Technology and CAD
hardware Trojan,  machine learning,  integrated circuit,  feature extraction,  register-transfer level,  

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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.