Next Article in Journal
Semilocal Convergence Domain of a Chandrasekhar Integral Equation
Previous Article in Journal
Analyzing Network Stability via Symmetric Structures and Domination Integrity in Signed Fuzzy Graphs
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Symmetry
Volume 17
Issue 5
10.3390/sym17050769
symmetry-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Full-Element Analysis of Side-Channel Leakage Dataset on Symmetric Cryptographic Advanced Encryption Standard

by
Weifeng Liu
1,2,
Wenchang Li
3,4,*,
Xiaodong Cao
1,2,
Yihao Fu
5,
Juping Wu
3,4,
Jian Liu
6,
Aidong Chen
5,
Yanlong Zhang
7,
Shuo Wang
7 and
Jing Zhou
7
1
Artificial Intelligence and High-Speed Circuits Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Key Laboratory of Solid-State Optoelectronic Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
4
College of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
5
Multi-Agent Systems Research Center, School of Robotics, Beijing Union University, Beijing 100101, China
6
State Key Laboratory of Semiconductor Physics and Chip Technologies, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
7
Beijing Institute of Microelectronics Technology, Beijing 100076, China
*
Author to whom correspondence should be addressed.
Symmetry 2025, 17(5), 769; https://doi.org/10.3390/sym17050769
Submission received: 30 March 2025 / Revised: 30 April 2025 / Accepted: 6 May 2025 / Published: 15 May 2025
(This article belongs to the Special Issue Applications Based on Symmetry and Asymmetry in Deep Learning and Artificial Intelligence Methods)
Versions Notes

Abstract

The application of deep learning in side-channel analysis faces critical challenges arising from dispersed public datasets—i.e., datasets collected from heterogeneous sources and platforms with varying formats, labeling schemes, and sampling settings—and insufficient sample distribution uniformity, characterized by imbalanced class distributions and long-tailed label samples. This paper presents a systematic analysis of symmetric cryptographic AES side-channel leakage datasets, examining how these issues impact the performance of deep learning-based side-channel analysis (DL-SCA) models. We analyze over 10 widely used datasets, including DPA Contest and ASCAD, and highlight key inconsistencies via visualization, statistical metrics, and model performance evaluations. For instance, the DPA_v4 dataset exhibits extreme label imbalance with a long-tailed distribution, while the ASCAD datasets demonstrate missing leakage features. Experiments conducted using CNN and Transformer models show that such imbalances lead to high accuracy for a few labels (e.g., label 14 in DPA_v4) but also extremely poor accuracy (<0.5%) for others, severely degrading generalization. We propose targeted improvements through enhanced data collection protocols, training strategies, and feature alignment techniques. Our findings emphasize that constructing balanced datasets covering the full key space is vital to achieving robust and generalizable DL-SCA performance. This work contributes both empirical insights and methodological guidance for standardizing the design of side-channel datasets.
Keywords: symmetric cipher; side-channel analysis; public datasets; deep learning; feature engineering symmetric cipher; side-channel analysis; public datasets; deep learning; feature engineering

Share and Cite

MDPI and ACS Style

Liu, W.; Li, W.; Cao, X.; Fu, Y.; Wu, J.; Liu, J.; Chen, A.; Zhang, Y.; Wang, S.; Zhou, J. Full-Element Analysis of Side-Channel Leakage Dataset on Symmetric Cryptographic Advanced Encryption Standard. Symmetry 2025, 17, 769. https://doi.org/10.3390/sym17050769

AMA Style

Liu W, Li W, Cao X, Fu Y, Wu J, Liu J, Chen A, Zhang Y, Wang S, Zhou J. Full-Element Analysis of Side-Channel Leakage Dataset on Symmetric Cryptographic Advanced Encryption Standard. Symmetry. 2025; 17(5):769. https://doi.org/10.3390/sym17050769

Chicago/Turabian Style

Liu, Weifeng, Wenchang Li, Xiaodong Cao, Yihao Fu, Juping Wu, Jian Liu, Aidong Chen, Yanlong Zhang, Shuo Wang, and Jing Zhou. 2025. "Full-Element Analysis of Side-Channel Leakage Dataset on Symmetric Cryptographic Advanced Encryption Standard" Symmetry 17, no. 5: 769. https://doi.org/10.3390/sym17050769

APA Style

Liu, W., Li, W., Cao, X., Fu, Y., Wu, J., Liu, J., Chen, A., Zhang, Y., Wang, S., & Zhou, J. (2025). Full-Element Analysis of Side-Channel Leakage Dataset on Symmetric Cryptographic Advanced Encryption Standard. Symmetry, 17(5), 769. https://doi.org/10.3390/sym17050769

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop