| S-box(Substitution box,denoted by S-Box),which is a main part of block cipher,is an non-linearly components of encryption algorithm.The property of cryptography depends on the strength of the non-linearity of S-Box.As the high non-linearity of neural network is in high accordance with the properties of cipher,it becomes possible to apply neural network to S-box.In addition,multiplier is one of the arithmetic unit of the hardcore processor,digital signal processor,filters,and high performance micro controller and other devices.It can provide powerful mathematical operations for carrying out real-time signal processings.This paper presents a scheme based on neural network to implement S-Box and multiplication respectively,which decreases the difficulty of implementing S-Box and multiplication.This paper is organized as follows: In chapter one,a brief history of ANN,some basic knowledge of boolean function and the recent situation of S-box are introduced.The significance of implementing S-box based on neural network is also introduced.In chapter two,the framework and method of implementing the boolean functions in S-box based on neural network is discussed.Differing from the previous network models,the proposed network,which can be used to implement any Boolean function in S-box,consists of multiple neural network perceptrons,and each perceptron only has a low number of input variables.Therefore,it is able to train the weight and threshold values of the networks through DNA-like learning algorithm.In the third chapter,the method of implementing the boolean functions in multiplier based on neural network is introduced.The method reduces the difficulty of implementing multiplication,and it can also be used to achieve boolean functions of higher-dimension.In chapter four,the dynamical properties of S-box are discussed.The corresponding state-transition graph and the linear matrix of S-Box are given,so is the periodic rings and the transient numbers of the S boxes.At last,chapter five makes a brief summary on this thesis and points out the research directions in future study. |