密码学中影响树奇偶校验机同步时间的因素

M. Aleksandrov, Y. Bashkov
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引用次数: 2

摘要

本文给出了树奇偶校验机同步时间影响因素的实验结果。提出了树奇偶校验机作为对称加密算法的改进。该方法的优点之一是利用神经网络的相互同步现象为用户生成相同的加密密钥,而无需传输。确定了影响神经网络同步时间和密钥加密强度水平的因素。通过实验确定了各因素的影响程度。确定了学习规则对神经网络同步时间和稳定性的影响。结果确定了神经网络互学习的最佳规则为Hebb规则,当神经网络的结构变得更复杂时,应首先增加隐藏神经元的数量。明确了进一步研究的任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Factors Affecting Synchronization Time of Tree Parity Machines in Cryptography
This article presents experimental results of evaluating factors affecting synchronization time of tree parity machines. Tree parity machines are proposed as a modification of the symmetric encryption algorithm. One of the advantages of the method consists in using the phenomenon of mutual synchronization of neural networks to generate an identical encryption key for users without the need to transfer it. As a result, the factors influencing the synchronization time of neural networks and the level of key cryptographic strength were determined. The degree of influence factors was found out experimentally. The influence of the learning rule on timing and stability of synchronization of neural networks was also determined. As a result, it was determined that the best rule for mutual learning of neural networks is Hebb’s rule, and when the architecture of neural networks becomes more complex, the number of hidden neurons should be increased first. The tasks of further research are defined.
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