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Paper 2020/655

Push For More: On Comparison of Data Augmentation and SMOTE With Optimised Deep Learning Architecture For Side-Channel

Yoo-Seung Won and Dirmanto Jap and Shivam Bhasin

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.

Note: We add the Acknowledgement to revised version.

Metadata
Available format(s)
PDF
Category
Secret-key cryptography
Publication info
Preprint. MINOR revision.
Keywords
Oversampling techniqueSide-channel analysisDeep learning
Contact author(s)
yooseung won @ ntu edu sg,djap @ ntu edu sg,sbhasin @ ntu edu sg
History
2020-06-03: revised
2020-06-03: received
See all versions
Short URL
https://ia.cr/2020/655
License
Creative Commons Attribution
CC BY
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