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.
|Journal||IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers|
|Publication status||Published - 2020 Nov 2|
|Event||39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States|
Duration: 2020 Nov 2 → 2020 Nov 5
ASJC Scopus subject areas
- Computer Science Applications
- Computer Graphics and Computer-Aided Design