On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

Conference Paper (2021)
Author(s)

Lichao Wu (TU Delft - Cyber Security)

Guilherme Perin (TU Delft - Cyber Security)

Research Group
Cyber Security
Copyright
© 2021 L. Wu, G. Perin
DOI related publication
https://doi.org/10.1007/978-3-030-81645-2_8
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 L. Wu, G. Perin
Research Group
Cyber Security
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
114-132
ISBN (print)
9783030816445
Reuse Rights

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Abstract

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural networks contain at least three types of layers: convolutional, pooling, and dense layers. Although the definition of pooling layers causes a large impact on neural network performance, a study on pooling hyperparameters effect on side-channel analysis is still not provided in the academic community. This paper provides extensive experimental results to demonstrate how pooling layer types and pooling stride and size affect the profiling attack performance with convolutional neural networks. Additionally, we demonstrate that pooling hyperparameters can be larger than usually used in related works and still keep good performance for profiling attacks on specific datasets.

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