Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security: A Hybrid Ensemble Classification Framework

Network Intrusion Detection Systems (NIDS) play a vital role in safeguarding Internet of Things (IoT) environments against escalating security threats. As attack vectors become increasingly sophisticated, traditional detection methods struggle to maintain effectiveness, especially when managing the vast quantities of heterogeneous data generated by IoT networks. This research introduces a novel Hybrid Ensemble Classification (HEC) framework that integrates advanced machine learning and deep learning techniques to enhance intrusion detection performance in IoT security. Our approach addresses key challenges through three primary innovations: (1) a Weighted Correlation Analysis technique that dynamically adapts feature selection thresholds based on statistical properties of datasets; (2) an enhanced Synthetic Minority Over-sampling Technique with Entropy-based Neighborhood Calibration to address class imbalance issues; and (3) a hierarchical ensemble architecture that combines temporal pattern analysis via Gated Recurrent Units with Attention and feature space optimization through an enhanced Random Forest algorithm. Comprehensive evaluations across UNSW-NB15, CIC-IDS2018, and IoTID20 datasets demonstrate that our proposed framework consistently outperforms state-of-the-art approaches, achieving accuracy improvements of up to $3.91 \%$ and F1-score gains of up to $4.16 \%$, while maintaining computational efficiency suitable for resource-constrained IoT environments.