Multi-Tier Ensemble based Approach for Threat Detection in IoT Security

With the rise of IoT devices, security vulnerabilities have increased, making traditional measures inadequate. Soft computing and machine learning offer adaptive threat detection by identifying patterns and anomalies. This paper presents a hierarchical ensemble-based approach for IoT security, where multiple classifiers collaborate to detect various threats. Trained on diverse datasets, these classifiers enhance accuracy through ensemble learning. Evaluated on a public dataset, the proposed approach outperforms state-of-the-art methods in accuracy, precision, and recall. It ensures high detection rates with low computational costs, making it a promising solution for securing IoT networks against cyber threats.