Memristive hyperchaotic system-based complex-valued artificial neural synchronization for secured communication in Industrial Internet of Things

Abstract

In this article, a memristive hyperchaotic system-based complex-valued Artificial Neural Network (ANN) synchronization for secured communication between Industrial Internet of Things (IIoT) nodes is proposed. The IIoT, an extension of the Internet of Things (IoT), has significant promise for resource use in the industrial sector and intelligent transformation. Security and privacy issues exist when industrial data is sent over an open network via sensor devices. For this, a secure key exchange protocol is necessary. Synchronized key exchange using an ANN is one of the solutions. Additionally, there is a dearth of research on the reciprocating training of ANNs and the use of a robust Pseudo-Random Number Generator (PRNG) to generate a shared input. This study explains how to use a hyperchaotic environment to swiftly assess how well ANNs finalize their coordination and synchronize for session key switch over. Reciprocating learning is used to synchronize two neural networks and transfer the ANN’s output across a public channel. When the ANNs have produced the same outputs in previous iterations, coordination is tested using that function. The proposed technique has lot of benefits, like: (1) using a hyperchaotic-guided PRNG to produce the ANN input sequences; (2) generating the session key via the public network using a reciprocal neuronal alignment of complex ANNs; and (3) allowing two communicating partners to identify complete synchronization more quickly when compared to earlier methodologies. The tested proposed technique outperforms comparable strategies in the literature, according to the results.