Machine Learning and Hardware security: Challenges and Opportunities -Invited Talk-

Francesco Regazzoni, Shivam Bhasin, Amir Ali Pour, Ihab Alshaer, Furkan Aydin, Aydin Aysu, Vincent Beroulle, Giorgio Di Natale, Paul Franzon, David Hely, Naofumi Homma, Akira Ito, Dirmanto Jap, Priyank Kashyap, Ilia Polian, Seetal Potluri, Rei Ueno, Elena Ioana Vatajelu, Ville Yli-Mayry

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Machine learning techniques have significantly changed our lives. They helped improving our everyday routines, but they also demonstrated to be an extremely helpful tool for more advanced and complex applications. However, the implications of hardware security problems under a massive diffusion of machine learning techniques are still to be completely understood. This paper first highlights novel applications of machine learning for hardware security, such as evaluation of post quantum cryptography hardware and extraction of physically unclonable functions from neural networks. Later, practical model extraction attack based on electromagnetic side-channel measurements are demonstrated followed by a discussion of strategies to protect proprietary models by watermarking them.

Original languageEnglish
Article number9256522
JournalIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers
Volume2020-November
DOIs
Publication statusPublished - 2020 Nov 2
Externally publishedYes
Event39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States
Duration: 2020 Nov 22020 Nov 5

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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