A Hybrid Ensemble Framework for Intrusion Detection in IoT Using Blockchain-Integrated Fog Networks

Abstract

The exponential growth of IoT has become a source of concern regarding cybersecurity vulnerabilities in real time and distributed environments, the research proposes a novel Fog computing-based hybrid ensemble framework using Blockchain technology to enhance IoT network security. It proposes advanced data preprocessing, feature selection, and hybrid ensemble learning that leads to remarkable performance: 99.5% accuracy, 99.2% precision, 99.4% recall, and a false positive rate of just 0.05% on the UNSW-NB15 dataset. Blockchain integration ensures secure and immutable logging of detected threats, further enhancing trust in the system. The scalability and robustness of the proposed framework are demonstrated in its ability to process high-traffic IoT networks while guaranteeing optimal resource efficiency. These results make the proposed approach a state-of-the-art solution for real-time attack detection in IoT networks, which can meet modern challenges in cybersecurity.