Paper 2020/655 Original: eprint.iacr.org/2020/655 ↗
Push For More: On Comparison of Data Augmentation and SMOTE With Optimised Deep Learning Architecture For Side-Channel
Abstract
Side-channel analysis has seen rapid adoption of deep learning techniques over the past years. While many paper focus on designing efficient architectures, some works have proposed techniques to boost the efficiency of existing architectures. These include methods like data augmentation, oversampling, regularization etc. In this paper, we compare data augmentation and oversampling (particularly SMOTE and its variants) on public traces of two side-channel protected AES. The techniques are compared in both balanced and imbalanced classes setting, and we show that adopting SMOTE variants can boost the attack efficiency in general. Further, we report a successful key recovery on ASCAD(desync=100) with 180 traces, a 50% improvement over current state of the art.
Metadata
- Available format(s)
PDF- Category
- Secret-key cryptography
- Publication info
- Preprint.
- History
- 2020-06-03: last updated
- 2020-06-03: published
- Short URL
- https://ia.cr/2020/655
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2020/655,
author = {Yoo-Seung Won and Dirmanto Jap and Shivam Bhasin},
title = {Push For More: On Comparison of Data Augmentation and SMOTE With Optimised Deep Learning Architecture For Side-Channel},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/655},
year = {2020},
url = {https://eprint.iacr.org/2020/655}
}