文献类型：会议论文

英文题名：An Asynchronous Encryption Arithmetic Based on Laguerre Chaotic Neural Networks

作者：Zou, Ajin[1];Xiao, Xiuchun[1]

机构：[1]Guangdong Ocean Univ, Informat Coll, Zhanjiang 524088, Peoples R China

会议论文集：1st WRI Global Congress on Intelligent Systems (GCIS 2009)

会议日期：MAY 19-21, 2009

会议地点：Xiamen, PEOPLES R CHINA

语种：英文

外文关键词：Feedforward neural networks - Intelligent systems - Orthogonal functions - Polynomials

外文摘要：Based on best square approximation theory, new feed-forward neural networks are introduced where hidden units activation functions employ Laguerre orthogonal polynomials Use these neural networks as the identifier model of the chaotic time series. Then, by varying the chaotic initial value and inputting to the networks, can produce new chaotic series, which are close to the theoretical values. We extract a subsequence as same length as the plaintext from the chaotic series and sort it. At last, by permuting the plaintext according to the sorted results of the subsequence, we can achieve the ciphertext. In the encryption system, the security of it depends completely on the complexity and unpredictability of the chaos. Especially, by varying the chaotic initial value, we can implement asynchronous "one-time pad cipher" encryption. The theoretical analysis and encryption instances proved that our arithmetic is useful, simple and high security, and it also has many advantages that a synchronous system can never achieve