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http://dx.doi.org/10.13089/JKIISC.2021.31.6.1127

Non-Profiling Power Analysis Attacks Using Continuous Wavelet Transform Method  

Bae, Daehyeon (Hoseo University)
Lee, Jaewook (Hoseo University)
Ha, Jaecheol (Hoseo University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.31, no.6, 2021 , pp. 1127-1136 More about this Journal
Abstract
In the field of power analysis attacks, electrical noise and misalignment of the power consumption trace are the major factors that determine the success of the attack. Therefore, several studies have been conducted to overcome this problem, and one of them is a signal processing method based on wavelet transform. Up to now, discrete wavelet transform, which can compress the trace, has been mostly used for power side-channel power analysis because continuous wavelet transform techniques increase data size and analysis time, and there is no efficient scale selection method. In this paper, we propose an efficient scale selection method optimized for power analysis attacks. Furthermore, we show that the analysis performance can be greatly improved when using the proposed method. As a result of the CPA(Correlation Power Analysis) and DDLA(Differential Deep Learning Analysis) experiments, which are non-profiling attacks, we confirmed that the proposed method is effective for noise reduction and trace alignment.
Keywords
Implementation Attack; Hardware Security; Artificial Intelligence; Deep Learning; Wavelet Transform;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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