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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