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

被引:5
|
作者
Khan, Burhan Ul Islam [1 ]
Goh, Khang Wen [2 ]
Khan, Abdul Raouf [3 ]
Zuhairi, Megat F. [4 ]
Chaimanee, Mesith [5 ]
机构
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Technol, Kuala Lumpur 50603, Malaysia
[2] INTI Int Univ, Fac Data Sci & Informat Technol, Nilai 71800, Malaysia
[3] King Faisal Univ, Dept Comp Sci, Al Hasa 31982, Saudi Arabia
[4] Univ Kuala Lumpur, Malaysian Inst Informat Technol, Kuala Lumpur 50250, Malaysia
[5] Shinawatra Univ, Fac Engn & Technol, Pathum Thani 12160, Thailand
关键词
IoT security; data confidentiality; smart cities; neural network optimization; Ethereum blockchain; artificial intelligence (AI); cybersecurity; INTERNET; THINGS; MODEL;
D O I
10.3390/pr12091825
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
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.
引用
收藏
页数:28
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