Cryptology ePrint Archive: Report 2018/1213

Deep Learning vs Template Attacks in front of fundamental targets: experimental study

Yevhenii ZOTKIN and Francis OLIVIER and Eric BOURBAO

Abstract: This study compares the experimental results of Template Attacks (TA) and Deep Learning (DL) techniques called Multi Layer Perceptron (MLP) and Convolutional Neural Network (CNN), concurrently in front of classical use cases often encountered in the side-channel analysis of cryptographic devices (restricted to SK). The starting point regards their comparative effectiveness against masked encryption which appears as intrinsically vulnerable. Surprisingly TA improved with Principal Components Analysis (PCA) and normalization, honorably makes the grade versus the latest DL methods which demand more calculation power. Another result is that both approaches face high difficulties against static targets such as secret data transfers or key schedule. The explanation of these observations resides in cross-matching. Beyond masking, the effects of other protections like jittering, shuffling and coding size are also tested. At the end of the day the benefit of DL techniques, stands in the better resistance of CNN to misalignment.

Category / Keywords: Side channel, deep dearning, template attack

Date: received 18 Dec 2018, last revised 20 Dec 2018

Contact author: francis olivier at gemalto com

Available format(s): PDF | BibTeX Citation

Version: 20181223:223323 (All versions of this report)

Short URL: ia.cr/2018/1213


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