Paper 2021/357
AISY - Deep Learning-based Framework for Side-channel Analysis
Guilherme Perin, Lichao Wu, and Stjepan Picek
Abstract
The deep learning-based side-channel analysis represents an active research domain. While it is clear that deep learning enables powerful side-channel attacks, the variety of research scenarios often makes the results difficult to reproduce. In this paper, we present AISY - a deep learning-based framework for profiling side-channel analysis. Our framework enables the users to run the analyses and report the results efficiently while maintaining the results' reproducible nature. The framework implements numerous features allowing state-of-the-art deep learning-based analysis. At the same time, the AISY framework allows easy add-ons of user-custom functionalities.
Metadata
- Available format(s)
-
PDF
- Publication info
- Preprint. MINOR revision.
- Keywords
- Side-channel AnalysisDeep LearningFramework
- Contact author(s)
-
picek stjepan @ gmail com
g perin @ gmail com
l wu-4 @ tudelft nl - History
- 2021-03-18: revised
- 2021-03-18: received
- See all versions
- Short URL
- https://ia.cr/2021/357
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2021/357,
author = {Guilherme Perin and Lichao Wu and Stjepan Picek},
title = {{AISY} - Deep Learning-based Framework for Side-channel Analysis},
howpublished = {Cryptology {ePrint} Archive, Paper 2021/357},
year = {2021},
url = {https://eprint.iacr.org/2021/357}
}
