Chaos-Based Mutual Synchronization of Three-Layer Tree Parity Machine: A Session Key Exchange Protocol Over Public Channel

Abstract

In this paper, a chaos-based triple-layer tree parity machine (TLTPM)-guided neural synchronization has been proposed for the development of the public key exchange protocol. A special neural network structure called tree parity machine (TPM) is used for neural synchronization. Two TPMs accept the common input and different weight vectors and update the weights using the neural learning rule by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two TPMs become identical. These identical weights serve as a secret key. There is, however, hardly any investigation to investigate the randomness of the common input vector used in the synchronization process. In this paper, logistic chaos system-based TLTPM is proposed. For faster synchronization, this proposed TLTPM model uses logistic chaos-generated random common input vector. This proposed TLTPM model is faster and has better security than TPM with the same input, output, and hidden neurons. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and robust potential.