基于混沌神经网络和C-MD结构的带密钥哈希函数

陈立全; 朱宇航; 王宇; 秦中元; 马旸

doi:10.11959/j.issn.2096-109x.2023033

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基于混沌神经网络和C-MD结构的带密钥哈希函数

学术论文 | 更新时间：2024-06-03

- 基于混沌神经网络和C-MD结构的带密钥哈希函数
- New hash function based on C-MD structure and chaotic neural network
- 网络与信息安全学报 2023年9卷第3期页码：1-15
- 作者机构：
  
  1. 东南大学网络空间安全学院，江苏南京 210096
  2. 网络通信与安全紫金山实验室，江苏南京 211111
- 作者简介：
  
  [ "陈立全（1976- ），男，广西玉林人，东南大学教授、博士生导师，主要研究方向为密码与安全协议、物联网安全" ]
  [ "朱宇航（1998- ），男，安徽安庆人，东南大学硕士生，主要研究方向为数据安全与隐私保护，安全协议" ]
  [ "王宇（1995- ），男，黑龙江哈尔滨人，东南大学博士生，主要研究方向为混沌图像加密、密码学" ]
  [ "秦中元（1974- ），男，河南安阳人，博士，东南大学副教授，主要研究方向为人工智能安全、网络安全协议" ]
  [ "马旸（1980- ），男，江苏南京人，东南大学博士生，主要研究方向为安全协议、网络流量分析、网络空间安全威胁识别、信息安全系统" ]
- 基金信息：
  
  国家重点研发计划;The National Key R＆D Program of China(2020YFE0200600)
- DOI：10.11959/j.issn.2096-109x.2023033
  中图分类号： TP311
- 网络首发：2023-06，
  
  纸质出版：2023-06-25
- 稿件说明：
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陈立全, 朱宇航, 王宇, 等. 基于混沌神经网络和C-MD结构的带密钥哈希函数[J]. 网络与信息安全学报, 2023,9(3):1-15.

Liquan CHEN, Yuhang ZHU, Yu WANG, et al. New hash function based on C-MD structure and chaotic neural network[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 1-15.
陈立全, 朱宇航, 王宇, 等. 基于混沌神经网络和C-MD结构的带密钥哈希函数[J]. 网络与信息安全学报, 2023,9(3):1-15. DOI： 10.11959/j.issn.2096-109x.2023033.

Liquan CHEN, Yuhang ZHU, Yu WANG, et al. New hash function based on C-MD structure and chaotic neural network[J]. Chinese Journal of Network and Information Security, 2023, 9(3): 1-15. DOI： 10.11959/j.issn.2096-109x.2023033.

摘要

近年来，被广泛使用的MD5、SHA-1等哈希算法存在不同程度的安全隐患，现在通用的SHA-2算法迭代结构与SHA-1算法相同，使得其存在被攻破的可能性。而SHA-3由于其内部结构复杂，实现复杂度较高。设计并实现了基于混沌神经网络和C-MD（chaotic neural network-Merkle-Damgard）结构的带密钥哈希函数，为提高安全性改进了Merkle-Damgard结构，并提出C-MD结构，将该结构应用于哈希函数设计可以抵抗中间相遇攻击、多碰撞攻击以及针对长信息的第二原像攻击；使用混沌神经网络作为压缩函数，以提高哈希函数复杂度，增强函数的抗碰撞性，支持函数输出多种长度；设计一个明文预处理器，使用耦合映像格子产生与明文长度相关的混沌序列对明文进行填充，增强哈希函数抵抗长度扩展攻击的能力。仿真实验结果表明，提出的哈希函数效率优于 SHA-2、SHA-3 等的同类型混沌哈希函数，能够抵御第二原像攻击、多碰撞攻击和差分攻击等多种攻击方式，同时具有更好的抗碰撞性和映射均匀性。此外，提出的哈希函数可以输出不同长度的散列值，能够较好地应用在数字签名、密钥生成、基于哈希的消息认证码、确定性随机比特发生器等领域。

Abstract

In recent years

widely used hash algorithms such as MD5 and SHA-1 have been found to have varying degrees of security risks.The iterative structure of the SHA-2 algorithm is similar to that of SHA-1

making it vulnerable to attacks as well.Meanwhile

SHA-3 has a complex internal structure and low implementation efficiency.To address these issues

a keyed hash function was designed and implemented based on chaotic neural network and C-MD structure.The approach involved improving the Merkle-Damgard structure by proposing the chaotic neural network Merkle-Damgard (C-MD) structure.This structure can be used to design a hash function that can withstand attacks such as the middle attack

multiple collision attack

and second pre-image attack for long information.Besides

the chaotic neural network was used as the compression function to increase the complexity of the hash function and improve its collision resistance

while also enabling it to output multiple lengths.Moreover

a plaintext preprocessor was designed

which used the coupled image lattice to generate chaos sequence related to the length of the plaintext to fill the plaintext

thus enhancing the ability of the hash function to resist length expansion attacks.Simulation results demonstrate that the proposed hash function performs faster than SHA-2

SHA-3 and the same type of chaotic hash function proposed by Teh et al.It can resist second pre-image attack

multi-collision attack and differential attack

while also exhibiting better collision resistance and mapping uniformity.In addition

the proposed Hash function can output Hash values of different lengths

making it suitable for use in digital signature

key generation

Hash-based message authentication code

deterministic random bit generator

and other application fields.

关键词

Keywords

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