针对 SIMON 和 SIMECK 块密码的增强相关密钥差分神经区分器
Gao Wang1, Gaoli Wang1,2
1Shanghai Key Laboratory of Trustworthy Computing, Software Engineering Institute, East China Normal University, Shanghai, North Zhongshan Road, China.
PeerJ. Computer science
|December 9, 2024
概括
研究人员为SIMON和SIMECK密码开发了一种增强的相关密钥差异神经区分器. 这种方法通过使用加权偏差分数和两个输入差异来提高准确性,优于以前的相关关键差异神经区分器.
科学领域:
- 密码学 密码学 密码学 密码学
- 机器学习 机器学习
- 应用数学 应用数学 应用数学
背景情况:
- 深度学习已经应用于差异加密分析,尤其是Gohr在CRYPTO 2019上的Speck密码.
- 卢和其他人. 与SIMON和SIMECK密码相比,改进了相关密钥差分神经区分器.
研究的目的:
- 为SIMON和SIMECK开发一个框架,用于构建增强的相关关键差异神经区分器.
- 通过神经网络提高差异加密分析的准确性和效率.
主要方法:
- 引入了一种使用加权偏差分数的方法,用于有效的输入差异选择.
- 提出了使用两个输入差异的增强相关关键差异神经区分器.
- 将框架应用于各种SIMON和SIMECK区块密码.
主要成果:
- 对SIMON32/64基本相关键差分神经区分器 (3%和1.9%) 实现了显著的准确性改进.
- 增强的区分器超过了基本的区分器,增加了多个SIMON和SIMECK变体的准确性.
- 在13轮SIMON32/64,13轮SIMON48/96,14轮SIMON64/128和多轮SIMECK中报告的准确性增加.
结论:
- 拟议的框架和增强的区分器与现有方法相比,提供了更高的性能.
- 权重偏差分数和双输入差异是改善差异神经密码分析的有效策略.
- 这项研究证明了深度学习在推进对称密码的加密分析技术方面的潜力.
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