Hardware-trojan classification based on the structure of trigger circuits utilizing random forests

Tatsuki Kurihara, Nozomu Togawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, with the spread of Internet of Things (IoT) devices, embedded hardware devices have been used in a variety of everyday electrical items. Due to the increased demand for embedded hardware devices, some of the IC design and manufacturing steps have been outsourced to third-party vendors. Since malicious third-party vendors may insert malicious circuits, called hardware Trojans, into their products, developing an effective hardware Trojan detection method is strongly required. In this paper, we propose 25 hardware-Trojan features based on the structure of trigger circuits for machine-learning-based hardware Trojan detection. Combining the proposed features into 11 existing hardware-Trojan features, we totally utilize 36 hardware-Trojan features for classification. Then we classify the nets in an unknown netlist into a set of normal nets and Trojan nets based on the random-forest classifier. The experimental results demonstrate that the average true positive rate (TPR) becomes 63.6% and the average true negative rate (TNR) becomes 100.0%. They improve the average TPR by 14.7 points while keeping the average TNR compared to existing state-of-the-art methods. In particular, the proposed method successfully finds out Trojan nets in several benchmark circuits, which are not found by the existing method.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433709
DOIs
Publication statusPublished - 2021 Jun 28
Event27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021 - Virtual, Online
Duration: 2021 Jun 282021 Jun 30

Publication series

NameProceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021

Conference

Conference27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
CityVirtual, Online
Period21/6/2821/6/30

Keywords

  • hardware security
  • hardware Trojan
  • machine learning
  • netlist
  • random forest

ASJC Scopus subject areas

  • Software
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture

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