IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Online ISSN : 1745-1337
Print ISSN : 0916-8508
Regular Section
Register-Transfer-Level Features for Machine-Learning-Based Hardware Trojan Detection
Hau Sim CHOOChia Yee OOIMichiko INOUENordinah ISMAILMehrdad MOGHBELChee Hoo KOK
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2020 Volume E103.A Issue 2 Pages 502-509

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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.

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© 2020 The Institute of Electronics, Information and Communication Engineers
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