Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147149
Title: Push for more : on comparison of data augmentation and SMOTE with optimised deep learning architecture for side-channel
Authors: Won, Yoo-Seung
Jap, Dirmanto
Bhasin, Shivam
Keywords: Engineering::Computer science and engineering::Information systems::Information systems applications
Issue Date: 2020
Source: Won, Y., Jap, D. & Bhasin, S. (2020). Push for more : on comparison of data augmentation and SMOTE with optimised deep learning architecture for side-channel. The 21st World Conference on Information Security Applications (WISA 2020), 12583 LNCS, 227-241. https://dx.doi.org/10.1007/978-3-030-65299-9_18
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.
URI: https://hdl.handle.net/10356/147149
ISBN: 9783030652982
DOI: 10.1007/978-3-030-65299-9_18
Rights: © 2020 Springer Nature Switzerland AG. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:TL Conference Papers

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