Cryptology ePrint Archive: Report 2019/1477

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

Daan van der Valk and 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.

Category / Keywords: Side-channel analysis, Deep learning, Neural networks, Representation learning

Date: received 22 Dec 2019

Contact author: picek stjepan at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20191223:152824 (All versions of this report)

Short URL: ia.cr/2019/1477


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