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A Stronger Leakage Model Based on Conditional Generative Adversarial Networks for Correlation Power Analysis

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成果类型:
期刊论文
作者:
Cheng Tang;Lang Li*;Yu Ou
通讯作者:
Lang Li
作者机构:
College of Computer Science and Technology, Hengyang Normal University, Hengyang, China
Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China
[Cheng Tang; Lang Li; Yu Ou] College of Computer Science and Technology, Hengyang Normal University, Hengyang, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China
通讯机构:
[Lang Li] C
College of Computer Science and Technology, Hengyang Normal University, Hengyang, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China
语种:
英文
关键词:
correlation power analysis;deep learning;leakage model;side-channel attacks
期刊:
International Journal of Circuit Theory and Applications
ISSN:
0098-9886
年:
2025
基金类别:
This research is supported by the Hunan Provincial Natural Science Foundation of China(2022JJ30103), “The 14th Five-Year Plan” Key Disciplines and Application-Oriented Special Disciplines of Hunan Province (Xiangjiaotong [2022] 351), and the Science and Technology Innovation Program of Hunan Province (2016TP1020).
机构署名:
本校为第一且通讯机构
院系归属:
计算机科学与技术学院
物理与电子工程学院
摘要:
Deep learning-based side-channel attacks (DL-SCA) have attracted widespread attention in recent years, and most of the researchers are devoted to finding the optimal DL-SCA method. At the same time, traditional SCA methods have lost their luster. However, traditional attacks still have certain advantages. Compared with the DL-SCA method, they do not require cumbersome engineering of tuning DL models and hyperparameters, making them easier to implement. Correlation power analysis (CPA), as a traditional SCA method, is still widely used in various analysis scenarios and plays an important role. ...

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