A comparison of weight initializers in deep learning-based side-channel analysis

Conference paper (2020)

Authors

H. Li Cyber Security - EEMCS
M. Krcek Cyber Security - EEMCS
G. Perin Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Deep learning Side-channel analysis Weight initialization
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Published Date

2020

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

The usage of deep learning in profiled side-channel analysis requires a careful selection of neural network hyperparameters. In recent publications, different network architectures have been presented as efficient profiled methods against protected AES implementations. Indeed, completely different convolutional neural network models have presented similar performance against public side-channel traces databases. In this work, we analyze how weight initializers’ choice influences deep neural networks’ performance in the profiled side-channel analysis. Our results show that different weight initializers provide radically different behavior. We observe that even high-performing initializers can reach significantly different performance when conducting multiple training phases. Finally, we found that this hyperparameter is more dependent on the choice of dataset than other, commonly examined, hyperparameters. When evaluating the connections with other hyperparameters, the biggest connection is observed with activation functions.