Evaluation on Hardware-Trojan Detection at Gate-Level IP Cores Utilizing Machine Learning Methods

Tatsuki Kurihara, Kento Hasegawa, 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 hardware Trojans into their products, developing an effective hardware Trojan detection method is strongly required. In this paper, we evaluate hardware Trojan detection methods using neural networks and random forests at gate-level intellectual property (IP) cores that contain more than 10,000 nets. First, we extract 11 features for each net in a given netlist, and learn them with neural networks and random forests. Then, we classify the nets in an unknown netlist into a set of normal nets and Trojan nets based on the learned classifiers. The experimental results demonstrate that the average true positive rate becomes 84.6% and the average true negative rate becomes 95.1%, which is sufficiently high accuracy compared to existing evaluations.

Original languageEnglish
Title of host publicationProceedings - 2020 26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728181875
DOIs
Publication statusPublished - 2020 Jul
Event26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020 - Virtual, Online, Italy
Duration: 2020 Jul 132020 Jul 16

Publication series

NameProceedings - 2020 26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020

Conference

Conference26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020
CountryItaly
CityVirtual, Online
Period20/7/1320/7/16

Keywords

  • gate-level netlist
  • hardware Trojan
  • machine learning
  • neural network
  • random forest

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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