基于神经网络混沌吸引子的公钥密码算法安全性分析及其实现

介绍一种基于神经网络混沌吸引子的Diffie-Hellman公钥密码算法.在过饱和贮存的Hopfield神经网络模型中混沌吸引子与初始状态之间存在一种单向函数关系,如果改变该神经网络的联结权矩阵,混沌吸引子及其所应的初始状态吸引域会随之发生改变.因此,我们可以其联结权矩阵为陷门,利用可交换的随机变换矩阵来改变神经网络的联结权矩阵,实现一种新的Diffie-Hellman公钥加密算法,即将随机变换矩阵作为私钥,而将变换后的神经网络联结突触矩阵作为公钥.为了说明这种新公钥加密方案的实用性,本文还分析和讨论其安全性和加密效率,并利用Java编程实现互联网的应用方案.实验结果表明,本算法是可行的,并具有较高的数据加密和解密速度.

Security Analysis of Public-key Cryptography Based on Chaotic Attractors of Neural Networks and Its Implementation

A new public-key cryptography based on chaotic attractors of neural networks is described in the paper.There is a one-way function between chaotic attractors and initial states in an Overstoraged Hopfield Neural Network(OHNN).If the neural synaptic matrix is changed with permutation operations,each attractor and its corresponding domain of attraction are simultaneously changed too.So we regard the neural synaptic matrix as a trap door and change it using commutative random permutation matrix.A new cryptography technique according to Diffie-Hellman public-key cryptosystem can be implemented.In the new scheme,the random permutation operation of the neural synaptic matrix is regarded as the secret key,while the neural synaptic matrix after permutation is regarded as public key.In order to explain the practicality of the proposed scheme,security and encryption efficiency of the new scheme are analyzed and discussed.The application scheme for Internet based on the proposed cryptography is implemented by using Java program.The experimental results show that the proposed cryptography is feasible and has a higher performance of encryption and decryption speed.