Paper 2021/525

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

Lichao Wu and Guilherme Perin

Abstract

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Specifically, profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction, the absence of manual feature selection, and the inherent ability to reduce 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. Finally, with a larger pooling stride and size, a neural network can be reduced in size, favoring training performance.