Annotations for symmetric probabilistic encryption algorithm based on chaotic attractors of neural networks  

作　　者：刘年生 郭东辉 

机构地区：[1]School of Computer Engineering,Jimei University [2]School of Information Science and Technology,Xiamen University

出　　处：《Optoelectronics Letters》2010年第1期57-60,共4页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (No.60076015);the Key Science Project of Fujian Provincein China (No.2009H0037);the Science Project of Xiamen City in China (No.3502Z20081073);the Foundation for Young Professors of Jimei University in China (No.2006B003)

摘　　要：The security of the symmetric probabilistic encryption scheme based on chaotic attractors of neural networks is analyzed and discussed. Firstly, the key uniqueness is proved by analyzing the rotation transform matrix to avoid the attack of the equivalent key. Secondly, the distributed uniformity of the numbers "0" and "1" in the corresponding attracting domain for every chaotic attractor is analyzed by the statistics method. It is testified that the distributed uniformity can be kept if the synaptic matrix of the neural network is changed by a standard permutation matrix. Two annotations based on the results above are proposed to improve the application security of the encryption algorithm.The security of the symmetric probabilistic encryption scheme based on chaotic attractors of neural networks is analyzed and discussed. Firstly, the key uniqueness is proved by analyzing the rotation transform matrix to avoid the attack of the equivalent key. Secondly, the distributed uniformity of the numbers ＂0＂ and ＂1＂ in the corresponding attracting domain for every chaotic attractor is analyzed by the statistics method. It is testified that the distributed uniformity can be kept if the synaptic matrix of the neural network is changed by a standard permutation matrix. Two annotations based on the results above are proposed to improve the application security of the encryption algorithm.

关 键 词：概率神经网络 混沌吸引子 对称加密 子算法 分布均匀性 注解 变换矩阵 统计方法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O415.5[自动化与计算机技术—控制科学与工程]