Design and investigation of a chaotic neural network architecture for cryptographic applications

Abstract

Artificial neural networks are an integral part of emerging technologies, and ongoing research has shown that they can be applied to a variety of applications. This paper proposes a new cryptographic algorithm using chaotic neural networks, whose function is enhanced by construction with polynomials that exhibit chaos, namely, nonlinear Hermite and Chebyshev polynomials. These polynomials incorporate a memristor conductance, which is used as an activation function in the chaotic neural networks. Further, a function of the weights obtained from the chaotic neural networks, is used to generate the initial values that are used in the cryptographic process. The encryption algorithm employed here is inspired by the Lai–Massey block cipher with cubic and two-dimensional logistic maps, and the evaluation of these chaotic equations is performed using correlation values. The correlation values between the cipher and plain text are also examined to determine the undecipherability of the message to be sent on a public channel.