Cryptography by Synchronization of Hopfield Neural Networks that Simulate Chaotic Signals Generated by the Human Body In this work, an asymmetric cryptography method for information security was developed, inspired by the fact that the human body generates chaotic signals, and these signals can be used to create sequences of random numbers. Encryption circuit was implemented in a Reconfigurable Hardware (FPGA). To encode and decode an image, the chaotic synchronization between two dynamic systems, such as Hopfield neural networks (HNNs), was used to simulate chaotic signals. The notion of Homotopy, an argument of topological nature, was used for the synchronization. The results show efficiency when compared to state of the art, in terms of image correlation, histogram analysis and hardware implementation.

An Efficient Cryptosystem based on the LogisticChebyshev Map We utilise the LogisticChebyshev map as a cascade discretetime chaotic system to design a new form of a cryptosystem. Based on the LogisticChebyshev map, we present a novel method for constructing substitution boxes or Sboxes. The performance of the proposed Sbox scheme is investigated. Our results shows that the constructed Sbox has significant qualities for viable applications in security related domains. Additionally, we utilise a new pseudorandom number generator (PRNG) scheme for the purpose of generating keystreams for further encryption. We utilise this technique to demonstrate how a PRNG based chaotic system can be utilsied for image encryption. Our simulation results and analysis show that the proposed scheme has efficient performance for cryptographic based image processing applications.