LUO Kaiwen,WANG Min,WANG Yi,et al.Side-Channel Data Preprocessing using Enhanced Convolutional Autoencoders[J].Journal of Chengdu University of Information Technology,2025,40(01):49-55.[doi:10.16836/j.cnki.jcuit.2025.01.008]
基于改进卷积自编码器的侧信道数据预处理
- Title:
- Side-Channel Data Preprocessing using Enhanced Convolutional Autoencoders
- 文章编号:
- 2096-1618(2025)01-0049-07
- 分类号:
- TP309
- 文献标志码:
- A
- 摘要:
- 近年来,基于深度学习的侧信道分析方法展现出强大的潜力与优势,然而目前大多数的深度学习技术主要集中在处理侧信道信息分类问题上。为进一步提高侧信道攻击的效果,采用改进的卷积自编码器对ASCAD数据集进行数据预处理。经过卷积自编码器的降维处理,ASCAD数据NICV泄露表现出明显的提升。为验证预处理效果,选择MLP攻击模型对数据集进行攻击效果检验。实验结果表明,经过改进的卷积自编码器模型预处理的ASCAD数据在侧信道攻击中表现出优于传统PCA预处理的更高性能,证明卷积自编码器在侧信道数据预处理方面的有效性和优越性。
- Abstract:
- In recent years, deep learning-based side-channel analysis methods have demonstrated significant potential and advantages. However, most of the current deep learning techniques are primarily focused on solving side-channel information classification problems.To further enhance the effectiveness of side-channel attacks, this paper employs an improved convolutional autoencoder for preprocessing the ASCAD dataset. Through the dimensionality reduction process of the convolutional autoencoder, the ASCAD data exhibits a notable improvement in normalized inter-class variance(NICV)leakage. To verify the preprocessing effectiveness, we selected an MLP attack model to assess the attack performance on the preprocessed dataset. The experiments confirmed that our convolutional autoencoder-based preprocessing outperforms the traditional PCA preprocessing, yielding superior performance in side-channel attacks. These results underscore the effectiveness and superiority of the convolutional autoencoder in side-channel data preprocessing.
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备注/Memo
收稿日期:2023-08-07