Paper 2021/242 Original: eprint.iacr.org/2021/242 ↗
GAP: Born to Break Hiding
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.
Metadata
- Available format(s)
PDF- Category
- Secret-key cryptography
- Publication info
- Preprint.
- History
- 2021-03-02: last updated
- 2021-03-02: published
- Short URL
- https://ia.cr/2021/242
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2021/242,
author = {Ju-Hwan Kim and Ji-Eun Woo and Soo-Jin Kim and So-Yeon Park and Dong-Guk Han},
title = {GAP: Born to Break Hiding},
howpublished = {Cryptology {ePrint} Archive, Paper 2021/242},
year = {2021},
url = {https://eprint.iacr.org/2021/242}
}