基于RBF神经网络和混沌映射的Hash函数构造

Construct Hash Function Based on RBF Neural Network and Chaotic Map

单向Hash函数在数字签名、身份认证和完整性检验等方面得到广泛的应用,也是现代密码领域中的研究热点。本文中,首先利用神经网络来训练一维非线性映射产生的混沌序列,然后利用改序列构造带秘密密钥的Hash函数,该算法的优点之一是神经网络隐藏混沌映射关系使得直接获得映射变得困难。模拟实验表明该算法具有很好的单向性、弱的碰撞性,较基于传统的Hash函数具有更强的保密性且实现简单。

How to design an efficient and security keyed bash function is always the point in modern cryptography researches. In this paper, A better chaotic sequence is generated by the RBF neural network through training the known chaotic sequence generated by a piecewise non-linear, then the sequence is used to construct keyed Hash function. One advantage of the algorithm is that the hidden-mapping model of neural network makes it difficult to get the direct mapping function of the ordinary chaos encryption algorithm. Simulation results show that the keyed Hash function based on the neural network has good one-way, weak collision, better security property and it can be realized easily.