A Blockchain-Driven, Machine Learning-Enabled Adaptive Security Framework for IoT Ecosystems

Authors

  • Chandra Bhushan Integral & Innovative Sustainable Education (IISE) College, Lucknow, Uttar Pradesh, INDIA.
  • Dr. Seema Tripathi International Institute for Special Education, Lucknow, Uttar Pradesh, INDIA.

DOI:

https://doi.org/10.55544/sjmars.icmri.6

Keywords:

Internet of Things (IoT), Blockchain Security, Machine Learning, Anomaly Detection, Hyperledger Fabric, Smart Contracts, Edge Computing, Adaptive Security

Abstract

The exponential growth of the Internet of Things (IoT) has revolutionized multiple domains—ranging from smart homes and healthcare monitoring to industrial automation and smart cities. Yet this proliferation of connected, resource-constrained devices has also dramatically expanded the attack surface, exposing networks to data tampering, device impersonation, denial-of-service (DoS) attacks, and unauthorized access. Traditional, centralized security measures struggle to keep pace with the dynamic and heterogeneous nature of IoT environments. In this paper, we propose a hybrid security framework that synergizes blockchain technology and machine learning (ML) to deliver a decentralized, tamper-resistant, and adaptive protection mechanism for IoT ecosystems. Blockchain provides immutable audit trails, decentralized trust, and programmable enforcement via smart contracts, while ML offers real-time anomaly detection and predictive threat analytics. We describe the architecture and workflows of our framework, outline our implementation using a permissioned Hyperledger Fabric network and edge-deployed ML models (including LSTM for sequential anomaly detection), and present simulation results showing over 97% detection accuracy, a false-positive rate below 3%, and acceptable transaction latencies (<1 s) on resource-constrained devices. We conclude that the integration of blockchain and ML yields a resilient security posture that can adapt autonomously to emerging threats, scale to millions of devices, and maintain low overhead on edge hardware.

References

[1] Christidis, K., & Devetsikiotis, M. (2016). Blockchains and Smart Contracts for the Internet of Things. IEEE Access, 4, 2292–2303.

[2] Dorri, A., Kanhere, S. S., & Jurdak, R. (2017). Blockchain in Internet of Things: Challenges and Solutions. arXiv preprint arXiv:1608.05187.

[3] Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep Learning for IoT Big Data and Streaming Analytics: A Survey. IEEE Communications Surveys & Tutorials, 20(4), 2923–2960.

[4] Sharma, P. K., Chen, J. H., & Park, J. H. (2018). A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT. IEEE Access, 6, 115–124.

[5] Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2021). Federated Learning for Smart Healthcare: A Survey. ACM Computing Surveys, 54(7), Article 141.

[6] Reyna, A., Martin, C., Chen, J., Soler, E., & Díaz, M. (2018). On Blockchain and Its Integration with IoT: Challenges and Opportunities. Future Generation Computer Systems, 88, 173–190.

[7] Pop, C., Seceleanu, C., & Crăciunescu, R. (2020). Lightweight Consensus Algorithms for IoT—Applications and Challenges. IEEE Access, 8, 137164–137190.

[8] Li, X., Jiang, P., Chen, T., Luo, X., & Wen, Q. (2020). A Survey on the Security of Blockchain Systems. Future Generation Computer Systems, 107, 841–853.

[9] Al-Kuwaiti, H. (2020). A Comprehensive Survey on Blockchain for IoT. IEEE Communications Surveys & Tutorials, 22(4), 2529–2556.

[10] Ullah, I., Khan, M. K., Aalsalem, M. Y., & Bangash, Y. A. (2019). Machine Learning for Cybersecurity in IoT: A Survey. IEEE Internet of Things Journal, 6(4), 6285–6304.

[11] Han, K., Mao, H., & Dally, W. J. (2016). Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. International Conference on Learning Representations (ICLR).

[12] Zyskind, G., Nathan, O., & Pentland, A. (2015). Decentralizing Privacy: Using Blockchain to Protect Personal Data. IEEE Security & Privacy, 16(4), 28–36.

[13] Kang, J., Yu, R., Huang, X., Maharjan, S., Zhang, Y., & Hossain, E. (2019). Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks. IEEE Internet of Things Journal, 6(3), 4660–4670.

[14] Zhang, Y., & Chen, M. (2020). Blockchain and Deep Reinforcement Learning for Secure Resource Allocation in Edge Computing. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(5), 525–535.

[15] Kshetri, N. (2017). 1 Blockchain’s Roles in Meeting Key Supply Chain Management Objectives. International Journal of Information Management, 39, 80–89.

[16] Dorri, A., Kanhere, S. S., Jurdak, R., & Gauravaram, P. (2019). Blockchain for IoT Security and Privacy: The Case Study of a Smart Home. Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 618–623.

[17] Nguyen, G., Kim, K., & Kim, S. (2020). A Method for Detecting Anomalies in IoT Data Using Convolutional Neural Networks. Sensors, 20(1), 257.

[18] Li, S., Da Xu, L., & Zhao, S. (2018). The Internet of Things: A Survey. Information Systems Frontiers, 17(2), 243–259.

[19] Xu, X., Weber, I., & Staples, M. (2019). Architecture for Blockchain Applications. Springer.

[20] Wu, H., Xu, J., & Zheng, Z. (2021). Secure Machine-Learning-as-a-Service for IoT Devices: A Lightweight Framework. IEEE Internet of Things Journal, 8(7), 5737–5750.

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Published

2025-07-27

How to Cite

Bhushan, C., & Tripathi, S. (2025). A Blockchain-Driven, Machine Learning-Enabled Adaptive Security Framework for IoT Ecosystems. Stallion Journal for Multidisciplinary Associated Research Studies, 1(1), 36–40. https://doi.org/10.55544/sjmars.icmri.6

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