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Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

Sohaib Latif1,*, M. Saad Bin Ilyas1, Azhar Imran2, Hamad Ali Abosaq3, Abdulaziz Alzubaidi4, Vincent Karovič Jr.5

1 Department of Computer Science, The University of Chenab, Gujrat, 50700, Pakistan
2 Department of Creative Technologies, Air University, Islamabad, 42000, Pakistan
3 Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran, 66244, Saudi Arabia
4 Department of Computer Science, Umm Alqura University, AlQunfudah, 28821, Saudi Arabia
5 Department of Information Systems, Faculty of Management, Comenius University in Bratislava, Bratislava, 820 05, Slovakia

* Corresponding Author: Sohaib Latif. Email: email

Intelligent Automation & Soft Computing 2024, 39(2), 353-379. https://doi.org/10.32604/iasc.2024.047080

Abstract

The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were also unable to detect denial-of-service (DoS) attacks. This study introduced a novel decentralized and secure framework for blockchain integration. To avoid single-point OF failure, an accredited access control scheme is incorporated, combining blockchain with local peers to record each transaction and verify the signature to access. Blockchain-based attribute-based cryptography is implemented to protect data storage privacy by generating threshold parameters, managing keys, and revoking users on the blockchain. An innovative contract-based DOS attack mitigation method is also incorporated to effectively validate devices with intelligent contracts as trusted or untrusted, preventing the server from becoming overwhelmed. The proposed framework effectively controls access, safeguards data privacy, and reduces the risk of cyberattacks. The results depict that the suggested framework outperforms the results in terms of accuracy, precision, sensitivity, recall, and F-measure at 96.9%, 98.43%, 98.8%, 98.43%, and 98.4%, respectively.

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APA Style
Latif, S., Ilyas, M.S.B., Imran, A., Abosaq, H.A., Alzubaidi, A. et al. (2024). Machine learning empowered security and privacy architecture for iot networks with the integration of blockchain. Intelligent Automation & Soft Computing, 39(2), 353-379. https://doi.org/10.32604/iasc.2024.047080
Vancouver Style
Latif S, Ilyas MSB, Imran A, Abosaq HA, Alzubaidi A, Jr. VK. Machine learning empowered security and privacy architecture for iot networks with the integration of blockchain. Intell Automat Soft Comput . 2024;39(2):353-379 https://doi.org/10.32604/iasc.2024.047080
IEEE Style
S. Latif, M.S.B. Ilyas, A. Imran, H.A. Abosaq, A. Alzubaidi, and V.K. Jr. "Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain," Intell. Automat. Soft Comput. , vol. 39, no. 2, pp. 353-379. 2024. https://doi.org/10.32604/iasc.2024.047080



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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