An Intelligent Framework for Secure IoT Communication Using Machine Learning-Based Intrusion Detection and Blockchain Authentication

The exponential growth of Internet of Things (IoT) devices has introduced significant security challenges due to resource constraints and heterogeneous communication protocols. Traditional security mechanisms are inadequate to handle sophisticated cyber-attacks such as Distributed Denial of Service (DDoS), spoofing, and data tampering in IoT networks. This paper proposes a novel hybrid framework that integrates Machine Learning-based Intrusion Detection System (ML-IDS) with Blockchain-based authentication to enhance security, integrity, and scalability in IoT communication. The ML-IDS module employs Random Forest and Deep Neural Network classifiers trained on the UNSW-NB15 and IoT-23 datasets to detect anomalous traffic with 98.2% accuracy. Simultaneously, a lightweight blockchain consensus mechanism ensures decentralized device authentication and tamper-proof data logging. Experimental results demonstrate that the proposed framework reduces detection latency by 32% and improves throughput by 18% compared to existing approaches, while maintaining low computational overhead suitable for edge devices. The framework provides a robust solution for secure IoT deployments in smart cities, healthcare, and industrial automation.