A blockchain-enabled edge computing framework leveraging artificial neural network and aquila optimization to enhance security and scalability of cloud-based IoT platforms

Abstract

This work proposes a robust convergence-based approach that integrates blockchain and edge computing, supporting a proactive and adaptive secure-optimized framework to fortify security measures and mitigate unauthorized access in IoT systems. In the developed framework the blockchain technology enforces proactive security by ensuring data integrity, decentralized authentication, and resilient threat prevention. While the fusion of an Artificial Neural Network (ANN) and Aquila Optimizer (AO), enables the framework to enhance the accuracy of threat detection by fine-tuning parameters and optimizing the network’s architecture. The ANN adeptly captures intricate patterns and features from input data, while the AO intelligently adjusts critical hyperparameters to foster swift convergence and achieve peak network performance. This integration empowers a robust and accurate security detection system, enabling effective identification of complex and multifaceted security threats. Through extensive experimental analyses, we validate the effectiveness of the proposed ANN-AO framework, demonstrating a reduction in latency by 587 ms, 1187 ms, 1079 ms, and 460 ms, while improving security detection by 17.00%, 41.00%, 11.00%, and 8.00%, when compared to cutting-edge security methods. Moreover, the results are further verified and validated through statistical and computational analyses.