Capuchin Search Algorithm With Deep Learning-Based Data Edge Verification for Blockchain-Assisted IoT Environment

Khaled H. Alyoubi; Adil O. Khadidos; Abdulrhman M. Alshareef; Diaa Hamed; Alaa O. Khadidos; Mahmoud Ragab

doi:10.1109/ACCESS.2023.3346437

IEEE Access (Jan 2024)

Capuchin Search Algorithm With Deep Learning-Based Data Edge Verification for Blockchain-Assisted IoT Environment

Khaled H. Alyoubi,
Adil O. Khadidos,
Abdulrhman M. Alshareef,
Diaa Hamed,
Alaa O. Khadidos,
Mahmoud Ragab

Affiliations

Khaled H. Alyoubi: Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Adil O. Khadidos: ORCiD; Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Abdulrhman M. Alshareef: ORCiD; Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Diaa Hamed: Faculty of Earth Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
Alaa O. Khadidos: ORCiD; Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Mahmoud Ragab: ORCiD; Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

DOI: https://doi.org/10.1109/ACCESS.2023.3346437
Journal volume & issue: Vol. 12
pp. 351 – 360

Abstract

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Internet of Things (IoT) devices generate enormous quantities of data, and ensuring the authenticity and integrity of this data is essential. Blockchain (BC) serves as a transparent and secure ledger for recording each data transaction, which prevents unauthorized modification and provides a trust layer for the IoT ecosystem. Data edge verification for BC-enabled IoT platforms is a fundamental aspect of ensuring data trustworthiness, integrity, and security in the system. In such an environment, IoT devices generate vast amounts of data, and BC technology can be used to create a decentralized and immutable ledger that records all the transactions and data exchanges Machine learning (ML) in the context of BC and the IoT offers a powerful toolkit for optimizing and securing these technologies. It enables the analysis of vast and complex data generated by IoT devices, allowing for predictive maintenance, anomaly detection, and pattern recognition. ML also enhances security by developing intrusion detection systems and supporting smart contracts in BC networks. This article introduces a new Capuchin Search Algorithm with a Deep Learning based Data Edge Verification model (CSADL-DEVM) for the Blockchain assisted IoT platform. The purpose of the CSADL-DEVM technique is to integrate the BC with DL and hyperparameter tuning concepts for data edge verification in the IoT environment. In addition, IoT devices comprise a considerable level of decentralized decision-making ability, which accomplishes a consensus on the performance of intra-block transactions. In addition, the CSADL-DEVM technique applies the Elman recurrent neural network (ERNN) model for the identification and classification of faults. Moreover, the hyperparameters related to the ERNN model can be adjusted by the use of CSA which in turn better the detection results. A comprehensive set of simulations has been conducted to exhibit the enhanced results of the CSADL-DEVM method. The obtained outcomes exhibit the superior outcome of the CSADL-DEVM algorithm.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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