Cryptology ePrint Archive: Report 2020/939

DLDDO: Deep Learning to Detect Dummy Operations

JongHyeok Lee and Dong-Guk Han

Abstract: Recently, research on deep learning based side-channel analysis (DLSCA) has received a lot of attention. Deep learning-based profiling methods similar to template attacks as well as non-profiling-based methods similar to differential power analysis have been proposed. DLSCA methods have been proposed for targets to which masking schemes or jitter-based hiding schemes are applied. However, most of them are methods for finding the secret key, except for methods for preprocessing, and there are no studies on the target to which the dummy-based hiding schemes or shuffling schemes are applied. In this paper, we propose a DLSCA for detecting dummy operations. In the previous study, dummy operations were detected using the method called BCDC, but there is a disadvantage in that it is impossible to detect dummy operations for commercial devices such as an IC card. We consider the detection of dummy operations as a multi-label classification problem and propose a deep learning method based on CNN to solve it. As a result, it is possible to successfully perform detection of dummy operations on an IC card, which was not possible in the previous study.

Category / Keywords: applications / Hiding Countermeasure, Deep Learning, Multi-label classification, IC Card, Dummy Operation

Original Publication (in the same form): The 21st World Conference on Information Security Applications

Date: received 30 Jul 2020

Contact author: n_seeu at kookmin ac kr,christa@kookmin ac kr

Available format(s): PDF | BibTeX Citation

Version: 20200731:122620 (All versions of this report)

Short URL: ia.cr/2020/939


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