基于机器学习的分组密码结构识别

夏锐琪; 李曼曼; 陈少真

doi:10.11959/j.issn.2096-109x.2023040

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基于机器学习的分组密码结构识别

学术论文 | 更新时间：2024-06-03

- 基于机器学习的分组密码结构识别
- Identification on the structures of block ciphers using machine learning
- 网络与信息安全学报 2023年9卷第3期页码：79-89
- 作者机构：
- 作者简介：
  
  [ "夏锐琪（1997- ），男，江苏宝应人，信息工程大学硕士生，主要研究方向为分组密码安全性分析" ]
  [ "李曼曼（1986- ），女，河南开封人，信息工程大学讲师，主要研究方向为分组密码安全性分析" ]
  [ "陈少真（1967- ），女，江苏无锡人，信息工程大学教授、博士生导师，主要研究方向为分组密码安全性分析" ]
- 基金信息：
  
  河南省自然科学基金;The Natural Science Foundation of Henan Province(232300421394)
- DOI：10.11959/j.issn.2096-109x.2023040
  中图分类号： TP393
- 网络首发：2023-06，
  
  纸质出版：2023-06-25
- 稿件说明：
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夏锐琪, 李曼曼, 陈少真. 基于机器学习的分组密码结构识别[J]. 网络与信息安全学报, 2023,9(3):79-89.

Ruiqi XIA, Manman LI, Shaozhen CHEN. Identification on the structures of block ciphers using machine learning[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 79-89.
夏锐琪, 李曼曼, 陈少真. 基于机器学习的分组密码结构识别[J]. 网络与信息安全学报, 2023,9(3):79-89. DOI： 10.11959/j.issn.2096-109x.2023040.

Ruiqi XIA, Manman LI, Shaozhen CHEN. Identification on the structures of block ciphers using machine learning[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 79-89. DOI： 10.11959/j.issn.2096-109x.2023040.

摘要

加密算法的识别与区分是密码分析领域的重要组成部分，是密钥恢复技术的前提条件之一，随着人工智能技术的不断发展，利用机器学习、神经网络等技术研究密码分析问题日趋成熟，这为实现以加密算法为代表的密码体制识别技术提供了有效的思路与启发。通过研究机器学习的基本原理，从理论和实验两个角度对Feistel和SPN结构的多种常见分组密码算法进行了识别实验，解决了随机密钥条件下利用未知密文识别分组密码加密结构的问题。引入游程分布指标、特征分布函数和KL散度等概念，通过分析两种分组密码结构加密后的密文特征分布，推导随机密钥条件下两种结构密文游程分布表达式，利用KL散度计算分布的差异性，证明两种结构密文间存在差异性，论证了实验的可行性；根据理论结果，建立了随机森林和Adaboosting2种机器学习模型，对12种分组密码算法在随机密钥条件下全轮加密后的密文提取密文游程分布指标，按照同种结构单一算法和同种结构混合算法两种标准对不同结构分组密码算法进行了识别。实验结果显示，两组实验中对各个具体算法的结构识别准确率高于80%，较已有工作提高40%左右，有效解决了随机密钥情况下分组密码结构识别问题，严格证明了两种分组密码结构之间在密文扩散性、分布关系上确实存在可区分的差异性，也为密码算法设计和安全性评估提供一定的参考。

Abstract

Cryptographic identification is a critical aspect of cryptanalysis and a fundamental premise for key recovery.With the advancement of artificial intelligence

cryptanalysis based on machine learning has become increasingly mature

providing more effective methods and valuable insights for cryptographic identification.The distinguishability experiments were performed based on the Machine Learning to identify the structures of block ciphers in conditions of random keys.The identification of two structures of block ciphers from theoretical and experimental angles was studied.The differences of features in two structures’ cipher texts have been deduced by introducing the runs distribution index

feature distribution functions

KL-divergence

etc.After completing the feasibility research

experiments to identify the structures of two block ciphers using two Machine Learning models and the runs distribution index were conducted.The experiments were divided into two groups: single algorithm group and mixture algorithms group.It is found that the accuracy of both groups are more than 80%

which is around 40% higher than former work.The problem of identifying the structures of Block Ciphers in the conditions of random keys is solved in detail.Meanwhile

differences between the two structures of block ciphers are verified

which can serve as a reference for the design of cryptography algorithms.

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