Factors Affecting Synchronization Time of Tree Parity Machines in Cryptography

Abstract

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.