An Intelligent Multi-Stage GA–SVM Hybrid Optimization Framework for Feature Engineering and Intrusion Detection in Internet of Things Networks

The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false positive rate of 1.5%. Additionally, it effectively detects critical User-to-Root (U2R) attacks at a rate of 96.2% and Remote-to-Local (R2L) attacks at 95.8%. Performance tests validate the system’s scalability for networks with up to 2000 nodes, with detection latencies of 120 ms at 65% CPU utilization in small-scale deployments and 250 ms at 85% CPU utilization in large-scale scenarios. Parameter sensitivity analysis enhances model robustness, while false positive examination aids in reducing administrative overhead for practical deployment. This IDS offers an effective, scalable, and resource-efficient solution for real-world IoT system security, outperforming traditional approaches.