Cryptology ePrint Archive: Report 2021/242

GAP: Born to Break Hiding

Ju-Hwan Kim and Ji-Eun Woo and Soo-Jin Kim and So-Yeon Park and Dong-Guk Han

Abstract: Recently, Machine Learning (ML) is widely investigated in the side-channel analysis (SCA) community. As an artificial neural network can extract the feature without preprocessing, ML-based SCA methods relatively less rely on the attacker's ability. Consequently, they outperform traditional methods. Hiding is a countermeasure against SCA that randomizes the moments of manipulating sensitive data. Since hiding could disturb the neural network's learning, an attacker should design a proper architecture against hiding. In this paper, we propose inherently robust architecture against every kind of desynchronization. We demonstrated the proposed method with plenty of datasets, including open datasets. As a result, our method outperforms state-of-the-art on every dataset.

Category / Keywords: secret-key cryptography / Side-Channel Analysis, Deep Learning, Global Average Pooling, Desynchronization, Hiding

Date: received 1 Mar 2021

Contact author: zzzz2605 at kookmin ac kr

Available format(s): PDF | BibTeX Citation

Version: 20210302:204253 (All versions of this report)

Short URL: ia.cr/2021/242


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