Paper 2024/049
CL-SCA: A Contrastive Learning Approach for Profiled Side-Channel Analysis
Abstract
Side-channel analysis (SCA) based on machine learning, particularly neural networks, has gained considerable attention in recent years. However, previous works predominantly focus on establishing connections between labels and related profiled traces, primarily capturing label-related features while often overlooking the connections between traces of the same label. This results in the loss of some valuable information. Additionally, attack traces also contain valuable information that can be used in the training process to assist model learning. To address these issues, we propose a profiled SCA approach based on contrastive learning, named CL-SCA. This approach extracts features by emphasizing the similarities among traces, thereby improving the effectiveness of key recovery while maintaining the advantages of the original SCA approach. Through experiments on different datasets from various platforms, we demonstrate that CL-SCA significantly outperforms existing methods.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Published elsewhere. Major revision. IEEE Transactions on Information Forensics and Security
- DOI
- 10.1109/TIFS.2025.3570123
- Keywords
- Side-channel analysisContrastive learningNeural networksProfiled analysis
- Contact author(s)
-
annvliu @ 163 com
wanganl @ bit edu cn
sfsun @ bit edu cn
weicm @ bit edu cn
dyl19 @ bit edu cn
liehuangz @ bit edu cn - History
- 2025-05-18: last of 2 revisions
- 2024-01-12: received
- See all versions
- Short URL
- https://ia.cr/2024/049
- License
-
CC BY-NC-SA
BibTeX
@misc{cryptoeprint:2024/049,
author = {Annv Liu and An Wang and Shaofei Sun and Congming Wei and Yaoling Ding and Yongjuan Wang and Liehuang Zhu},
title = {{CL}-{SCA}: A Contrastive Learning Approach for Profiled Side-Channel Analysis},
howpublished = {Cryptology {ePrint} Archive, Paper 2024/049},
year = {2024},
doi = {10.1109/TIFS.2025.3570123},
url = {https://eprint.iacr.org/2024/049}
}
