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@article{203592,
        author = {Amritesh Sanjay Bhoyar and Rushikesh Deepak Thakre and Adish Ajay Gujarathi and Himanish Abhijit More and Mrs. Vandana Dixit},
        title = {IoT Security using Blockchain and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12208-12213},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203592},
        abstract = {The rapid expansion of the Internet of Things (IoT) has introduced severe security vulnerabilities due to its reliance on centralized architectures and resource-constrained devices. To address these limitations, this paper proposes a hybrid, lightweight framework converging Blockchain and Machine Learning (ML) to inherently secure IoT ecosystems. The system employs an Ethereum-based distributed ledger (via Smart Contracts) to ensure immutable data logging and decentralized access control. Simultaneously, an edge-based ML engine utilizes Isolation Forests for real-time, multidimensional anomaly detection against sensor deviations and power-profiling cyber-attacks. By linking this ML-based intrusion detection directly with a blockchain-driven dynamic device trust scoring mechanism, the framework establishes an automated, self-healing network. Experimental simulations establish that this integrated approach successfully mitigates data tampering and Sybil attacks with minimal edge computational overhead, delivering a robust, scalable, and decentralized security architecture suitable for modern IoT deployments.},
        keywords = {Internet of Things (IoT), Blockchain, Machine Learning, Edge Computing, Anomaly Detection, Dynamic Trust Evaluation, Security Framework.},
        month = {May},
        }