Paper 2022/1737
Regularizers to the Rescue: Fighting Overfitting in Deep Learning-based Side-channel Analysis
Abstract
Despite considerable achievements of deep learning-based side-channel analysis, overfitting represents a significant obstacle in finding optimized neural network models. This issue is not unique to the side-channel domain. Regularization techniques are popular solutions to overfitting and have long been used in various domains. At the same time, the works in the side-channel domain show sporadic utilization of regularization techniques. What is more, no systematic study investigates these techniques' effectiveness. In this paper, we aim to investigate the regularization effectiveness on a randomly selected model, by applying four powerful and easy-to-use regularization techniques to eight combinations of datasets, leakage models, and deep learning topologies. The investigated techniques are $L_1$, $L_2$, dropout, and early stopping. Our results show that while all these techniques can improve performance in many cases, $L_1$ and $L_2$ are the most effective. Finally, if training time matters, early stopping is the best technique.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- Side-channel AnalysisDeep LearningRegularizationOverfittingASCONAES
- Contact author(s)
-
a rezaeezade-1 @ tudelft nl
lejla @ cs ru nl - History
- 2023-09-26: last of 2 revisions
- 2022-12-17: received
- See all versions
- Short URL
- https://ia.cr/2022/1737
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2022/1737,
author = {Azade Rezaeezade and Lejla Batina},
title = {Regularizers to the Rescue: Fighting Overfitting in Deep Learning-based Side-channel Analysis},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/1737},
year = {2022},
url = {https://eprint.iacr.org/2022/1737}
}
