Paper 2021/242
GAP: Born to Break Hiding
Ju-Hwan Kim, Ji-Eun Woo, Soo-Jin Kim, 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.
Metadata
- Available format(s)
-
PDF
- Category
- Secret-key cryptography
- Publication info
- Preprint. Minor revision.
- Keywords
- Side-Channel AnalysisDeep LearningGlobal Average PoolingDesynchronizationHiding
- Contact author(s)
- zzzz2605 @ kookmin ac kr
- History
- 2021-03-02: received
- Short URL
- https://ia.cr/2021/242
- License
-
CC BY
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},
note = {\url{https://eprint.iacr.org/2021/242}},
url = {https://eprint.iacr.org/2021/242}
}
