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

被引:6
|
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
  • [21] AI-based malware detection in IoT networks within smart cities: A survey
    Alhamdi, Mustafa J. M.
    Lopez-Guede, Jose Manuel
    Alqaryouti, Jafar
    Rahebi, Javad
    Zulueta, Ekaitz
    Fernandez-Gamiz, Unai
    COMPUTER COMMUNICATIONS, 2025, 233
  • [22] The Blockchain Random Neural Network for cybersecure IoT and 5G infrastructure in Smart Cities
    Serrano, Will
    JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2021, 175
  • [23] A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness
    Elkhodr, Mahmoud
    Khan, Samiya
    Gide, Ergun
    FUTURE INTERNET, 2024, 16 (01)
  • [24] Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China
    Wang, Ke
    Zhao, Yafei
    Gangadhari, Rajan Kumar
    Li, Zhixing
    SUSTAINABILITY, 2021, 13 (19)
  • [25] An overview of security and privacy in smart cities' IoT communications
    Al-Turjman, Fadi
    Zahmatkesh, Hadi
    Shahroze, Ramiz
    TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2022, 33 (03)
  • [26] The power of AI, IoT, and advanced quantum based optical systems in smart cities
    Rajkumar, N.
    Viji, C.
    Latha, Pandala Madhavi
    Vennila, V. Baby
    Shanmugam, Sathish Kumar
    Pillai, Nataraj Boothalingam
    OPTICAL AND QUANTUM ELECTRONICS, 2024, 56 (03)
  • [27] Software Engineering for IoT-Driven Data Analytics Applications
    Ahmad, Aakash
    Fahmideh, Mahdi
    Altamimi, Ahmed B.
    Katib, Iyad
    Albeshri, Aiiad
    Alreshidi, Abdulrahman
    Alanazi, Adwan Alownie
    Mehmood, Rashid
    IEEE ACCESS, 2021, 9 : 48197 - 48217
  • [28] Towards a conceptual framework for AI-driven anomaly detection in smart city IoT networks for enhanced cybersecurity
    Zeng, Heng
    Yunis, Manal
    Khalil, Ayman
    Mirza, Nawazish
    JOURNAL OF INNOVATION & KNOWLEDGE, 2024, 9 (04):
  • [29] Blockchain-based Big Data Integrity Service Framework for IoT Devices Data Processing in Smart Cities
    Alam, Tanweer
    MINDANAO JOURNAL OF SCIENCE AND TECHNOLOGY, 2021, 19 (01): : 137 - 162
  • [30] ANT-Centric IoT Security Reference Architecture-Security-by-Design for Satellite-Enabled Smart Cities
    Lam, Kwok-Yan
    Mitra, Sananda
    Gondesen, Florian
    Yi, Xun
    IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (08) : 5895 - 5908