Hardware Trojan detection and classification based on steady state learning

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, we propose a logic-testing based HT detection and classification method utilizing steady state learning. We first observe that HTs are hidden while applying random test patterns in a short time but most of them can be activated in a very long-term random circuit operation. Hence it is very natural that we learn steady signal-transition states of every suspicious Trojan net in a netlist by performing short-term random simulation. After that, we simulate or emulate the netlist in a very long time by giving random test patterns and obtain a set of signal-transition states. By discovering correlation between them, our method detects HTs and finds out its behavior. Experimental results demonstrate that our method can successfully identify all the real Trojan nets to be Trojan nets and all the normal nets to be normal nets, while other existing logic-testing HT detection methods cannot detect some of them.

本文言語English
ホスト出版物のタイトル2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ215-220
ページ数6
ISBN(電子版)9781538603512
DOI
出版ステータスPublished - 2017 9 19
イベント23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 - Thessaloniki, Greece
継続期間: 2017 7 32017 7 5

出版物シリーズ

名前2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017

Other

Other23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
CountryGreece
CityThessaloniki
Period17/7/317/7/5

ASJC Scopus subject areas

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

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