Cryptology ePrint Archive: Report 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.

Category / Keywords: secret-key cryptography / Oversampling technique, Side-channel analysis, Deep learning

Date: received 30 May 2020, last revised 3 Jun 2020

Contact author: yooseung won at ntu edu sg,djap@ntu edu sg,sbhasin@ntu edu sg

Available format(s): PDF | BibTeX Citation

Note: We add the Acknowledgement to revised version.

Version: 20200603:123657 (All versions of this report)

Short URL: ia.cr/2020/655


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