A self-learning framework to detect the intruded integrated circuits

F. K. Lodhi, I. Abbasi, F. Khalid, O. Hasan, F. Awwad, S. R. Hasan

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

29 Citations (Scopus)

Abstract

Globalization trends in integrated circuit (IC) design using deep submicron (DSM) technologies are leading to increased vulnerability of ICs against malicious intrusions. These malicious intrusions are referred as hardware Trojans. One way to address this threat is to utilize unique electrical signatures of ICs. However, this technique requires analyzing extensive sensor data to detect the intruded integrated circuits. In order to overcome this limitation, we propose to combine the signature extraction mechanism with machine learning algorithms to develop a self-learning framework that can detect the intruded integrated circuits. The proposed approach applies the lazy, eager or probabilistic learners to generate self-learning prediction model based on the electrical signatures. In order to validate this framework, we applied it on a recently proposed signature based hardware Trojan detection technique. The cross validation comparison of these learner shows that eager learners are able to detect the intrusion with 96% accuracy and also require less amount of memory and processing power compared to other machine learning techniques.

Original languageEnglish
Title of host publicationISCAS 2016 - IEEE International Symposium on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1702-1705
Number of pages4
ISBN (Electronic)9781479953400
DOIs
Publication statusPublished - Jul 29 2016
Event2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016 - Montreal, Canada
Duration: May 22 2016May 25 2016

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2016-July
ISSN (Print)0271-4310

Other

Other2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016
Country/TerritoryCanada
CityMontreal
Period5/22/165/25/16

Keywords

  • Asynchronous pipeline
  • Hardware Trojan
  • MSMA
  • Machine Learning
  • Rapid Miner Studio

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A self-learning framework to detect the intruded integrated circuits'. Together they form a unique fingerprint.

Cite this