基于神经网络模型的双混沌Hash函数构造

A Dual Chaotic Hash Function Based on Cellular Neural Network

高效快速的单向Hash函数是当前安全技术研究的热点。文章采用神经网络结构构造了一种Hash函数,由Logistic映射和Chebyshev映射结合起来的双混沌系统产生该神经网络的参数,将明文信息逐块进行处理,并最终通过异或产生128 bit的Hash值。经实验数据和仿真分析可知:文章提出的方案满足单向Hash函数所要求的混乱和置换特性,并且具有很好的弱碰撞性和初值敏感性;另外,该方案结构简单容易实现。

The Hash function with high speed and efifciency has been a hotspot of security. In this paper, a new Hash function based on cellular neural network was proposed. The parameters of the cellular neural network were produced by a unique system which combined the Logistic map with the Chebyshev map. The function can handle the plaintext by the block, and the ifnal 128 Hash value is the xor of every block’s Hash value. The experimental data and simulated analysis show that the proposed algorithm can satisfy the requirements of a secure hash function, and it has some good properties such as diffusion, confusion, weak collision and sensitivity to initial conditions. What’s more, the construction of the scheme can be achieved easily.