A novel encryption model for text messages using delayed chaotic neural network and DNA cryptography

Abstract

In this paper, a novel model for encrypting text messages using time varying delayed Hopfield neural network and a posterior DNA cryptographic model is proposed. The chaotic neural network applied here is used to generate a binary sequence which is later passed to a permutation function and generate the key for the first level encryption. The plaintext is converted to a corresponding binary sequence after a conversion to ASCI value and encrypted by switching of chaotic neural network maps and a permutation function which is dependent on the binary sequence generated from the chaotic neural network. An additional DNA cryptographic model is used over the cipher text obtained from the first level encryption to robust the security of the proposed model.