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
相关论文
共 50 条
  • [1] Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities
    Kamruzzaman, M. M.
    Yan, Bingxin
    Sarker, Md Nazirul Islam
    Alruwaili, Omar
    Wu, Min
    Alrashdi, Ibrahim
    JOURNAL OF HEALTHCARE ENGINEERING, 2022, 2022
  • [2] Enhanced Security of IoT Data Sharing Management by Smart Contracts and Blockchain
    Hoang-Anh Pham
    Trung-Kien Le
    Thi-Ngoc-My Pham
    Hoai-Quoc-Trung Nguyen
    Thanh-Van Le
    ISCIT 2019: PROCEEDINGS OF 2019 19TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES (ISCIT), 2019, : 398 - 403
  • [3] A framework for blockchain-based management of IoT-driven data sharing
    Alreshidi, Abdulrahman
    INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES, 2025, 12 (01): : 208 - 219
  • [4] IoT-Driven Automated Object Detection Algorithm for Urban Surveillance Systems in Smart Cities
    Hu, Ling
    Ni, Qiang
    IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (02): : 747 - 754
  • [5] LocateMyBus: IoT-Driven Smart Bus Transit
    Desingu, Karthik
    Isaac, Daniel Mark
    Mirunalini, P.
    Bharathi, B.
    Philipose, Cherry Mathew
    JOURNAL OF COMMUNICATIONS SOFTWARE AND SYSTEMS, 2023, 19 (02) : 136 - 146
  • [6] Integrating IoT-Driven Events into Business Processes
    Kirikkayis, Yusuf
    Gallik, Florian
    Seiger, Ronny
    Reichert, Manfred
    INTELLIGENT INFORMATION SYSTEMS, CAISE FORUM 2023, 2023, 477 : 86 - 94
  • [7] PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities
    Kumar, Prabhat
    Kumar, Randhir
    Srivastava, Gautam
    Gupta, Govind P.
    Tripathi, Rakesh
    Gadekallu, Thippa Reddy
    Xiong, Neal N.
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2021, 8 (03): : 2326 - 2341
  • [8] IoT and Blockchain in the Development of Smart Cities
    Khrais, Laith T.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (02) : 153 - 159
  • [9] Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development
    Alsaig, Alaa
    Alagar, Vangalur
    Chammaa, Zaki
    Shiri, Nematollaah
    SENSORS, 2019, 19 (11):
  • [10] IoT-driven optimization of a NxN enhanced pipeline multiplier
    Mohammad, Khader
    Al-Sheikh, Nirmeen
    COMPUTERS & ELECTRICAL ENGINEERING, 2024, 119