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混合Cuckoo搜索-蜂算法与Memristive混沌初始化用于加密强大的S-Box生成
1Software Engineering Department, Engineering Faculty, Firat University, 23319 Elazig, Turkey.
Biomimetics (Basel, Switzerland)
|September 26, 2025
概括
这项研究介绍了一种新型的混合Cuckoo Search-Bees算法 (CSBA),用于生成密码系统的高度非线性替换盒 (s-box). 通过创建具有卓越加密特性的强大的s-box,CSBA方法提高了加密安全性.
科学领域:
- 密码学 密码学 密码学 密码学
- 计算机科学 计算机科学
- 应用数学 应用数学 应用数学
背景情况:
- 替换盒 (s-box) 对加密系统的复杂性和弹性至关重要.
- 现有的s-box生成方法需要针对增强的非线性属性进行优化.
研究的目的:
- 提出一种混合的元启发算法,Cuckoo Search-Bees算法 (CSBA),用于生成具有强大的加密属性的s-box.
- 为了利用Memristive Lu混乱地图来实现高多样性的初始人口生成.
主要方法:
- 一种混合进化方法,结合了Cuckoo Search和Bees算法 (CSBA).
- 使用四维的Memristive Lu混乱地图用于初始的人口生成.
- 使用非线性评估s-box适应性,并使用CSBA和Bees算法进行优化.
主要成果:
- 拟议的CSBA算法在生成s-box方面表现出强的性能.
- 实现了109.75的平均非线性值,这表明成功率很高.
- 使用诸如SAC,非线性,BIC-SAC,BIC-NL,最大差异分布和线性均性 (LU) 等指标评估性能.
结论:
- 开发的元启发算法,特别是CSBA,对于生成强大可靠的s-box是有效的.
- 与其他用于s-box生成的元启发式方法相比,CSBA方法显示出更高的性能.
- 这项研究有助于通过先进的s-box设计增强对称加密算法的安全性.
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