Pseudo-random sequence generation using the CNN universal machine with applications to cryptography
Pseudo-random sequence generation using the CNN universal machine with applications to cryptography
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使用 CNN 通用机生成伪随机序列及其在密码学中的应用
DOI：
10.1109/cnna.1996.566613
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发表时间：
1996
期刊：
影响因子：
--
通讯作者：
Leon O. Chua
中科院分区：
文献类型：
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作者：
K. R. Crounse;Ta;Leon O. Chua
在物理系统仿真、通信和密码学等许多应用中，可再现的随机数据的良好来源非常重要。证明了细胞神经网络（CNN）通用机（或离散时间CNN）能够利用细胞自动机（CA）高速产生二维伪随机比特流。首先，利用平均场理论选择了一些不可逆的二维CA规则，通过一系列的统计检验分析了这些规则的随机性。其次，考虑了一类特殊的可逆CA用于随机数生成，并显示出具有类似物理模型的一些理想特性。最后，作为CNNUM上随机数生成的一个应用实例，提出了一些加密方案。
A good source of reproducible random-looking data is important in many applications ranging from simulation of physical systems, communications, and cryptography. It is demonstrated that the cellular neural network (CNN) universal machine (or the discrete-time CNN) is capable of producing a two-dimensional pseudo-random bit stream at high speeds by means of cellular automata (CA). First, the random properties of some irreversible two-dimensional CA rules, selected by applying mean-field theory, are analyzed by a battery of statistical tests. Second, a special class of reversible CA are considered for random number generation and are shown to have some of the desirable properties of physics-like models. Finally, as an example application for random number generation on the CNNUM, some cryptographic schemes are proposed.