Welcome to the resource topic for 2021/242
Title:
GAP: Born to Break Hiding
Authors: Ju-Hwan Kim, Ji-Eun Woo, Soo-Jin Kim, So-Yeon Park, 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.
ePrint: https://eprint.iacr.org/2021/242
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