Skip to main navigation Skip to search Skip to main content

A Neural Network Trojan Detection Method Based on Particle Swarm Optimization

  • School of Information Science and Engineering, Harbin Institute of Technology Weihai
  • Ministry of Natural Resources of the People's Republic of China

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

Abstract

Hardware Trojans (HTs) are malicious modifications of the original circuits intended to leak information or cause malfunction. Based on the Side Channel Analysis (SCA) technology, a set of hardware Trojan detection platform is designed for RTL circuits on the basis of HSPICE power consumption simulation. Principal Component Analysis (PCA) algorithm is used to reduce the dimension of power consumption data. An intelligent neural networks (NN) algorithm based on Particle Swarm Optimization (PSO) is introduced to achieve HTs recognition. Experimental results show that the detection accuracy of PSO NN method is much better than traditional BP NN method.

Original languageEnglish
Title of host publication2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018 - Proceedings
EditorsTing-Ao Tang, Fan Ye, Yu-Long Jiang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538644409
DOIs
StatePublished - 5 Dec 2018
Externally publishedYes
Event14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018 - Qingdao, China
Duration: 31 Oct 20183 Nov 2018

Publication series

Name2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018 - Proceedings

Conference

Conference14th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2018
Country/TerritoryChina
CityQingdao
Period31/10/183/11/18

Fingerprint

Dive into the research topics of 'A Neural Network Trojan Detection Method Based on Particle Swarm Optimization'. Together they form a unique fingerprint.

Cite this