Neural weight coordination-based vector-valued neural network synchronization

Abstract

In this paper, Harris’ Hawks weight optimization-guided artificial neural learning-based quicker session key coordination for Industrial Internet-of-Things (IIoT) to enhance the security of Critical Energy Infrastructure (CEI) is proposed. Transportation, telecommunications, healthcare, finance, and defense are all being revolutionized by the energy industry’s digitization. CEI is widely dispersed, resulting in complex cyber-physical networks that require constant monitoring and quick recovery to avoid cyberattacks. Substantial efforts were made in this regard to tackle the key exchange problem in IIoT devices, the majority of which have depended on traditional approaches. Existing solutions fail to adequately resolve the security and privacy issues that IIoT systems face. This study proposes a Triple Layer Vector-Valued Neural Network (TLVVNN) to cope with the problem. However, research into optimizing the value of neural weights for quicker neural synchronization is rare. In this case, Harris’ Hawks is used to optimizing the neural network’s weight vector for quicker coordination. The coordinated weight becomes the session key once this process is accomplished. This technique has several advantages, including (1) Generation of session key via mutual neural synchronization over the public channel. (2) It enables Harris’ Hawks-based neural weight vector optimization for faster neural synchronization across public channels. (3) Vector inputs and weights are taken into consideration for TLVVNN networks. (4) The internal structure of the TLVVNN is complex by three hidden layers. As a result, the attacker might have a lot of difficulties determining the internal architecture. (5) Several pairs of variable-length session keys are generated by TLVVNN. (6) It prevents impersonation, geometric, brute force, and majority assaults. Tests to validate the performance of the proposed methodology are carried out, and the results show that the proposed methodology outperforms similar approaches already in use.