{"title":"Design of S-boxes based on neural networks*","authors":"Mohammad Nourian Awal Noughabi, B. Sadeghiyan","doi":"10.1109/ICEIE.2010.5559741","DOIUrl":null,"url":null,"abstract":"In this paper, we present a framework for the design of S-boxes used in ciphers based on neural networks. It can yield S-boxes with different input and output length. The designed n × n S-boxes satisfy the desired cryptographic properties of non-linearity, completeness, strict avalanche, and output bits independence criteria. We propose a four layer topology, where the number of neurons, located at the input layer, is two times the number of input bits of the designed S-box and also, the number of neurons, located at the first hidden layer, is as equal as input layer neurons, while its second hidden layer included n/2 neurons, and its output layer included n neurons. The input value of the designed S-boxes consists of n-bit input vector and constant n-bit initial value (IV). We apply a Sigmoid nonlinear function as the activation function of our scheme. The values of weights were obtained through error back propagation learning algorithm, while a training set is used for learning. The used training set consists some different pairs of plaintexts and ciphertexts with AES's S-box. We also implement an 8 × 8 S-box based on neural networks with the essential security criteria. The results indicate that the proposed scheme can yield S-boxes with the desired cryptographic properties.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Electronics and Information Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEIE.2010.5559741","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
In this paper, we present a framework for the design of S-boxes used in ciphers based on neural networks. It can yield S-boxes with different input and output length. The designed n × n S-boxes satisfy the desired cryptographic properties of non-linearity, completeness, strict avalanche, and output bits independence criteria. We propose a four layer topology, where the number of neurons, located at the input layer, is two times the number of input bits of the designed S-box and also, the number of neurons, located at the first hidden layer, is as equal as input layer neurons, while its second hidden layer included n/2 neurons, and its output layer included n neurons. The input value of the designed S-boxes consists of n-bit input vector and constant n-bit initial value (IV). We apply a Sigmoid nonlinear function as the activation function of our scheme. The values of weights were obtained through error back propagation learning algorithm, while a training set is used for learning. The used training set consists some different pairs of plaintexts and ciphertexts with AES's S-box. We also implement an 8 × 8 S-box based on neural networks with the essential security criteria. The results indicate that the proposed scheme can yield S-boxes with the desired cryptographic properties.