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

Intelligent Automation & Soft Computing 2024年第2期39卷 353-379页

作者： sohaib latif M.Saad Bin Ilyas Azhar Imran Hamad Ali Abosaq Abdulaziz Alzubaidi Vincent Karovic Jr. Department of Computer Science The University of ChenabGujrat50700Pakistan Department of Creative Technologies Air UniversityIslamabad42000Pakistan Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran66244Saudi Arabia Department of Computer Science Umm Alqura UniversityAlQunfudah28821Saudi Arabia Department of Information Systems Faculty of ManagementComenius University in BratislavaBratislava82005Slovakia

The Internet of Things(IoT)is growing rapidly and impacting almost every aspect of our lives,fromwearables and healthcare to security,traffic management,and fleet management *** has generated massive volumes of data and security,and data privacy risks are increasing with the advancement of technology and network *** access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF ***,conventional privacy preservation methods have high latency costs and overhead for resource-constrained *** machine learning approaches were also unable to detect denial-of-service(DoS)*** study introduced a novel decentralized and secure framework for blockchain *** 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 ***-based attribute-based cryptography is implemented to protect data storage privacy by generating threshold parameters,managing keys,and revoking users on the *** 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 *** proposed framework effectively controls access,safeguards data privacy,and reduces the risk of *** 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.

关键词： Machine learning internet of things blockchain data privacy security Industry 4.0

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