Recurrent neural networks-guided vector-valued synchronized key exchange for secure and privacy-preserving communication in Industrial Internet of Things

Abstract

This paper presents an innovative approach to the problem of key exchange in the Industrial Internet of Things (IIoT) implementation. Recurrent Neural Networks (RNNs) and vector-valued neural synchronization are the key components of the proposed secure transmission method. Drive-response methods are integrated to improve speed in important applications, meeting the continuous need for effective cryptographic key exchange between IIoT devices. Conventional algorithms face drawn-out assessment procedures that jeopardize neural synchronization concealment. This work examines how random input vector generation and synchronization issues in RNNs with drive-response are affected by proportional and non-proportional delays. Furthermore, it delves into an unexplored domain, investigating the synchronization of response-based RNN systems without delays and drive-response-based RNN systems with various proportional delays. It also suggests a simplified analysis of Artificial Neural Networks (ANNs) synchronization, organizing ANNs for session key switch-over using an RNN system. To achieve polynomial and non-polynomial synchronization in the proposed driver response systems, this method offers several benefits: it introduces a polynomial synchronization theory for RNNs to generate synchronized input vectors for ANN synchronization; it uses inequality assessment techniques and Lyapunov formulas to derive relevant control inputs and time-dependent conditions; it establishes the relationship between polynomial and non-polynomial synchronization; and it provides numerical examples demonstrating its effectiveness. It also builds a neural network for generating session keys throughout the IIoT network by aligning vector-valued ANNs in a reciprocal manner. This strategy outperforms previous approaches in the literature, validated through simulations, balances resilience against attacks with minimal computational load, resulting in more effective and resilient industrial applications.