Hardware Trojans classification for gate-level netlists using multi-layer neural networks

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

41 Citations (Scopus)

Abstract

Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.

Original languageEnglish
Title of host publication2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages227-232
Number of pages6
ISBN (Electronic)9781538603512
DOIs
Publication statusPublished - 2017 Sep 19
Event23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 - Thessaloniki, Greece
Duration: 2017 Jul 32017 Jul 5

Publication series

Name2017 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

Keywords

  • Gate-level netlist
  • Hardware Trojan
  • Machine learning
  • Multi-layer
  • Neural network

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Hardware Trojans classification for gate-level netlists using multi-layer neural networks'. Together they form a unique fingerprint.

Cite this