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 analysisDeep learningNeural networksRepresentation learning
Contact author(s)
picek stjepan @ gmail com
History
2019-12-23: received
Short URL
https://ia.cr/2019/1477
License
Creative Commons Attribution
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},
      note = {\url{https://eprint.iacr.org/2019/1477}},
      url = {https://eprint.iacr.org/2019/1477}
}
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