Kilroy was here: The First Step Towards Explainability of Neural Networks in Profiled Side-channel Analysis

Daan van der Valk; Stjepan Picek; Shivam Bhasin

Paper 2019/1477

Kilroy was here: The First Step Towards Explainability of Neural Networks in Profiled Side-channel Analysis

Daan van der Valk, Stjepan Picek, and Shivam Bhasin

Abstract

While several works have explored the application of deep learning for efficient profiled side-channel analysis, explainability or in other words what neural networks learn remains a rather untouched topic. As a first step, this paper explores the Singular Vector Canonical Correlation Analysis (SVCCA) tool to interpret what neural networks learn while training on different side-channel datasets, by concentrating on deep layers of the network. Information from SVCCA can help, to an extent, with several practical problems in a profiled side-channel analysis like portability issue and criteria to choose a number of layers/neurons to fight portability, provide insight on the correct size of training dataset and detect deceptive conditions like over-specialization of networks.

Metadata

Available format(s): PDF
Publication info: Preprint. MINOR revision.
Keywords: Side-channel analysis Deep learning Neural networks Representation learning
Contact author(s): picek stjepan @ gmail com
History: 2019-12-23: received
Short URL: https://ia.cr/2019/1477
License: CC BY

BibTeX

@misc{cryptoeprint:2019/1477,
      author = {Daan van der Valk and Stjepan Picek and Shivam Bhasin},
      title = {Kilroy was here: The First Step Towards Explainability of Neural Networks in Profiled Side-channel Analysis},
      howpublished = {Cryptology {ePrint} Archive, Paper 2019/1477},
      year = {2019},
      url = {https://eprint.iacr.org/2019/1477}
}