Using Path Features for Hardware Trojan Detection Based on Machine Learning Techniques

Chia Heng Yen*, Jung Che Tsai, Kai Chiang Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

As the outsourcing process in the design and fabrication to third parties becomes more popular in the IC industry, the consciousness of hardware security has been rising these years. In this paper, we propose a novel method for hardware Trojan detection using specific path features at the gate level. In the training flow, path classifiers can be trained with SVM and RF algorithms using the path features from the trained circuits. In the classifying flow, an average of 0.96 on the F1-score in the results of the path classification demonstrates that logical paths can be easily classified into Trojan paths and Trojan-free paths with the trained path classifiers. In the localizing flow, the intersections between the logical paths can be favorable for precisely localizing the Trojan gates. As the FPRs are kept low to prevent normal gates from misclassifying into the Trojan gates, the high TPRs can be obtained for localizing the Trojan gates with the proposed scoring method.

Original languageEnglish
Title of host publicationProceedings of the 24th International Symposium on Quality Electronic Design, ISQED 2023
PublisherIEEE Computer Society
ISBN (Electronic)9798350334753
DOIs
StatePublished - 2023
Event24th International Symposium on Quality Electronic Design, ISQED 2023 - San Francisco, United States
Duration: 5 Apr 20237 Apr 2023

Publication series

NameProceedings - International Symposium on Quality Electronic Design, ISQED
Volume2023-April
ISSN (Print)1948-3287
ISSN (Electronic)1948-3295

Conference

Conference24th International Symposium on Quality Electronic Design, ISQED 2023
Country/TerritoryUnited States
CitySan Francisco
Period5/04/237/04/23

Fingerprint

Dive into the research topics of 'Using Path Features for Hardware Trojan Detection Based on Machine Learning Techniques'. Together they form a unique fingerprint.

Cite this