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SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

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成果类型:
期刊论文
作者:
Liu, Jiahui;Li, Lang;Li, Di;Ou, Yu
通讯作者:
Li, L
作者机构:
[Ou, Yu; Liu, Jiahui; Li, Lang; Li, Di] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
[Ou, Yu; Liu, Jiahui; Li, Lang; Li, Di] Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
通讯机构:
[Li, L ] H
Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
语种:
英文
关键词:
Side-channel analysis;correlation power analysis;genetic algorithm;crossover;mutation
期刊:
计算机、材料和连续体(英文)
ISSN:
1546-2218
年:
2024
卷:
79
期:
3
页码:
4641-4657
基金类别:
Hunan Provincial Natrual Science Foundation of China [2022JJ30103]; The 14th Five-Year" Key Disciplines and Application Oriented Special Disciplines of Hunan Province [351]; 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:
Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossove...

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