用于大状态分组密码的深度学习辅助密钥恢复框架

A deep learning-aided key recovery framework for large-state block ciphers

深度学习辅助密钥恢复攻击是2019年International Cryptology Conference(CRYPTO)上提出的一项全新密码分析技术.针对该技术至今无法应用于大状态分组密码的缺陷,本文提出了一种深度学习辅助的多阶段密钥恢复框架.该框架的核心是找到一个神经区分器组合,分阶段进行密钥恢复攻击.本文首先针对Speck的大状态成员分别训练了一组神经区分器,通过在该框架下利用区分器组合,设计并执行了实际密钥恢复攻击,证实了该框架的有效性.然后,提出了一种在低概率差分中寻找中性比特的方法,来把实际攻击扩展成覆盖更长轮数的理论攻击.最终,针对缩减轮Speck的最大状态成员取得了更好的密钥恢复攻击.这项工作为使用深度学习对更多分组密码进行密码分析铺平了道路.本文的验证代码已开源至https://github.com/AI-Lab-Y/NAAF.

The deep learning-aided key recovery attack is a new cryptanalysis technique published in CRYPTO’2019.The drawback of this technique is that it does not apply to large-state block ciphers.To overcome the drawback,this paper proposes a deep learning-based multistage key recovery framework.The core of this technique is to find a combination of neural distinguishers(NDs)for performing key recovery attacks at each stage.To apply this multistage key recovery framework to large-state members of Speck,multiple NDs are trained and combined into groups.Employing the groups of NDs under the multistage key recovery framework,practical attacks are designed and trialed to show framework effectiveness.The practical attacks are then extended to theoretical attacks,covering more rounds by prepending longer differentials before NDs.Moreover,to boost signals from NDs,an efficient algorithm is proposed to find neutral bits for differentials with low probability.Therefore,considerable improvement is observed in terms of both time and data complexities of differential key recovery attacks on round-reduced Speck with the largest state.This work paves the way for performing cryptanalysis using deep learning on more block ciphers.The related code is available at https://github.com/AI-Lab-Y/NAAF.