改进的深度学习辅助密钥恢复框架:大状态分组密码的应用
Improved deep learning aided key recovery framework:applications to large-state block ciphers
李肖伟 1任炯炯 1陈少真1
作者信息
- 1. 信息工程大学网络空间安全学院,中国 郑州市,450000
- 折叠
摘要
在 2019 年的年度国际密码学会议上,Gohr提出一种基于深度学习的密码分析技术,适用于分组较短的减轮轻量级分组密码SPECK32/64.Gohr遗留了一个关键问题,即如何实现基于深度学习的大状态分组密码密钥恢复攻击.本文设计了一种基于深度学习的大状态分组密码的密钥恢复框架.首先,提出基于深度学习的密钥比特敏感性测试(KBST)客观划分密钥空间.其次,提出一种新的构造神经区分器组合方法,以改进用于大状态分组密码深度学习辅助密钥恢复框架,并从密码分析角度证明其合理性和有效性.在改进的密钥恢复框架下,本文为SIMON和SPECK各大状态训练了一个有效的神经区分器组合,并执行了对SIMON和SPECK大状态成员的实际密钥恢复攻击.本文提出的 13轮SIMON64 攻击是迄今为止最有效的实际密钥恢复攻击方法.这是首次尝试在 18 轮 SIMON128、19 轮SIMON128、14 轮SIMON96 和 14 轮SIMON64 上进行基于深度学习的实用密钥恢复攻击.此外,本文改进了针对SPECK大状态成员的实际密钥恢复攻击结果,提高了密钥恢复攻击的成功率.
Abstract
At the Annual International Cryptology Conference in 2019,Gohr introduced a deep learning based cryptanalysis technique applicable to the reduced-round lightweight block ciphers with a short block of SPECK32/64.One significant challenge left unstudied by Gohr's work is the implementation of key recovery attacks on large-state block ciphers based on deep learning.The purpose of this paper is to present an improved deep learning based framework for recovering keys for large-state block ciphers.First,we propose a key bit sensitivity test(KBST)based on deep learning to divide the key space objectively.Second,we propose a new method for constructing neural distinguisher combinations to improve a deep learning based key recovery framework for large-state block ciphers and demonstrate its rationality and effectiveness from the perspective of cryptanalysis.Under the improved key recovery framework,we train an efficient neural distinguisher combination for each large-state member of SIMON and SPECK and finally carry out a practical key recovery attack on the large-state members of SIMON and SPECK.Furthermore,we propose that the 13-round SIMON64 attack is the most effective approach for practical key recovery to date.Noteworthly,this is the first attempt to propose deep learning based practical key recovery attacks on 18-round SIMON128,19-round SIMON128,14-round SIMON96,and 14-round SIMON64.Additionally,we enhance the outcomes of the practical key recovery attack on SPECK large-state members,which amplifies the success rate of the key recovery attack in comparison to existing results.
关键词
深度学习/大状态分组密码/密钥恢复/差分分析/SIMON/SPECKKey words
Deep learning/Large-state block cipher/Key recovery/Differential cryptanalysis/SIMON/SPECK引用本文复制引用
基金项目
National Natural Science Foundation of China(62206312)
出版年
2024
NETL
NSTL
万方数据