A Blockchain-Driven, Machine Learning-Enabled Adaptive Security Framework for IoT Ecosystems - Bhushan, Chandra; Tripathi, Seema (Stallion Journal for Multidisciplinary Associated Research Studies, 2025)
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.