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Article

Integrating AI and Blockchain for Enhanced Data Security in IoT-Driven Smart Cities

by
Burhan Ul Islam Khan
1,*,
Khang Wen Goh
2,
Abdul Raouf Khan
3,*,
Megat F. Zuhairi
4,* and
Mesith Chaimanee
5
1
Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
2
Faculty of Data Science and Information Technology, INTI International University, Nilai 71800, Malaysia
3
Department of Computer Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia
4
Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur 50250, Malaysia
5
Faculty of Engineering and Technology, Shinawatra University, Pathum Thani 12160, Thailand
*
Authors to whom correspondence should be addressed.
Processes 2024, 12(9), 1825; https://doi.org/10.3390/pr12091825
Submission received: 25 July 2024 / Revised: 19 August 2024 / Accepted: 23 August 2024 / Published: 27 August 2024
(This article belongs to the Section AI-Enabled Process Engineering)

Abstract

Blockchain is recognized for its robust security features, and its integration with Internet of Things (IoT) systems presents scalability and operational challenges. Deploying Artificial Intelligence (AI) within blockchain environments raises concerns about balancing rigorous security requirements with computational efficiency. The prime motivation resides in integrating AI with blockchain to strengthen IoT security and withstand multiple variants of lethal threats. With the increasing number of IoT devices, there has also been a spontaneous increase in security vulnerabilities. While conventional security methods are inadequate for the diversification of IoT devices, adopting AI can assist in identifying and mitigating such threats in real time, whereas integrating AI with blockchain can offer more intelligent decentralized security measures. The paper contributes to a three-layered architecture encompassing the device/sensory, edge, and cloud layers. This structure supports a novel method for assessing legitimacy scores and serves as an initial security measure. The proposed scheme also enhances the architecture by introducing an Ethereum-based data repositioning framework as a potential trapdoor function, ensuring maximal secrecy. To complement this, a simplified consensus module generates a conclusive evidence matrix, bolstering accountability. The model also incorporates an innovative AI-based security optimization utilizing an unconventional neural network model that operates faster and is enhanced with metaheuristic algorithms. Comparative benchmarks demonstrate that our approach results in a 48.5% improvement in threat detection accuracy and a 23.5% reduction in processing time relative to existing systems, marking significant advancements in IoT security for smart cities.
Keywords: IoT security; data confidentiality; smart cities; neural network optimization; Ethereum blockchain; artificial intelligence (AI); cybersecurity IoT security; data confidentiality; smart cities; neural network optimization; Ethereum blockchain; artificial intelligence (AI); cybersecurity

Share and Cite

MDPI and ACS Style

Khan, B.U.I.; Goh, K.W.; Khan, A.R.; Zuhairi, M.F.; Chaimanee, M. Integrating AI and Blockchain for Enhanced Data Security in IoT-Driven Smart Cities. Processes 2024, 12, 1825. https://doi.org/10.3390/pr12091825

AMA Style

Khan BUI, Goh KW, Khan AR, Zuhairi MF, Chaimanee M. Integrating AI and Blockchain for Enhanced Data Security in IoT-Driven Smart Cities. Processes. 2024; 12(9):1825. https://doi.org/10.3390/pr12091825

Chicago/Turabian Style

Khan, Burhan Ul Islam, Khang Wen Goh, Abdul Raouf Khan, Megat F. Zuhairi, and Mesith Chaimanee. 2024. "Integrating AI and Blockchain for Enhanced Data Security in IoT-Driven Smart Cities" Processes 12, no. 9: 1825. https://doi.org/10.3390/pr12091825

APA Style

Khan, B. U. I., Goh, K. W., Khan, A. R., Zuhairi, M. F., & Chaimanee, M. (2024). Integrating AI and Blockchain for Enhanced Data Security in IoT-Driven Smart Cities. Processes, 12(9), 1825. https://doi.org/10.3390/pr12091825

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