Improved deep learning aided key recovery framework: applications to large-state block ciphers

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.

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。