Abstract

The Internet of Things (IoT) advancement has created new security holes, which require intrusion detection systems to defend networks effectively. The complex structure of IoT networks causes traditional security methods to fail because they produce high amounts of incorrect detections and limited ability to accurately identify threats. The authors introduce ID-ELC: Ensemble Learning and Classification framework for Intrusion Detection, which aims to strengthen IoT environment security. A new ID-ELC model uses CS optimization with composite variance to choose network features that boost their detection capabilities. The cybersecurity evaluation of the system utilized Kyoto network records that included 91,000 intrusion-prone records and 59,000 benign logs from 150,000 total records. Experiments revealed ID-ELC surpasses Statistical Flow Features (SFF) and Two-layer Dimension Reduction and Two-tier Classification (TDRTC) through precision 0.98, accuracy 0.98, sensitivity 0.99 and specificity 0.97. Science-based evaluations confirm ID-ELC represents a flexible and resilient tool for IoT intrusion protection that shows practical value for citywide security systems and medicine networks and manufacturing operations. Future investigation will concentrate on enhancing the selection of features alongside classification methods to address rising cyber threats.