Enhancing the secrecy of a cryptographic key generated using synchronized artificial neural networks1

Abstract

The main options for the formation of a shared secret using synchronized artificial neural networks and possible patterns of behavior of a cryptanalyst are considered. To solve the problem of increasing the    confidentiality of the generated shared secret, if it is used as a cryptographic key, it is proposed to use the  mixing a certain number of results of individual synchronizations (convolution). As a mixing function, we consider the convolution of the vectors of network weights by bitwise addition modulo 2 of all the results of individual synchronizations. It is shown that the probability of success of a cryptanalyst is reduced exponentially with an increase of the number of terms in the convolution and can be chosen arbitrarily small. Moreover, the distribution law of the generated key after convolution is close to uniform and the uniformity increases with the number of terms in the convolution.