2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 1 , PP: 374-388, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
M. Mohan 1 * , R. Vijayakarthika 2 , M. Balakrishnan 3 * , R. Sundar 4 , T. Chithrakumar 5 , Vaishnavi V. 6
Doi: https://doi.org/10.54216/JISIoT.170126
The exponential growth of the Internet of Things (IoT) ecosystem has amplified concerns regarding data privacy, trust management, and cyber resilience in decentralized environments. Traditional perimeter-based security models are inadequate for heterogeneous IoT networks that operate across multiple domains. To address these challenges, this paper proposes a Blockchain-Augmented Zero Trust Architecture (BZTA) integrated with a hybrid intrusion detection mechanism for achieving secure, verifiable, and adaptive threat mitigation in decentralized IoT frameworks. The proposed BZTA employs blockchain-based identity verification to ensure device authenticity and policy-driven Zero Trust enforcement to validate every access request dynamically. A federated intrusion detection model built using Long Short-Term Memory (LSTM) and Graph Attention Networks (GAT) identifies anomalous communication patterns, while smart contracts facilitate tamper-proof logging and automated response coordination. The integration of Proof-of-Trust (PoT) consensus enhances scalability by minimizing latency during transaction validation. Experimental evaluations conducted on simulated IoT network datasets demonstrate a detection accuracy of 98.6%, false positive rate of 1.8%, and an average latency reduction of 22% compared to traditional IDS and standalone blockchain systems. The proposed BZTA framework effectively balances security, scalability, and interoperability, providing a resilient foundation for next-generation decentralized IoT infrastructures.
Blockchain , Zero Trust Architecture , Intrusion Detection System (IDS) , Internet of Things (IoT) , Graph Attention Network (GAT) , LSTM , Proof-of-Trust consensus , decentralized security , smart contracts , federated learning
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