Generalization-Aware Optimization-Based Feature Weight Learning for Hybrid Intrusion Detection Systems
DOI:
https://doi.org/10.15662/IJEETR.2026.0802071Keywords:
Intrusion Detection System, Feature Weight Learning, Hybrid Ensemble, Validation-Based Optimizatio, Generalization, Network SecurityAbstract
Intrusion Detection Systems (IDS) are widely used to identify malicious activities in network traffic, yet many machine learning methods still rely on fixed feature selection that does not change during training and can lead to overfitting. This work presents a Generalization-Aware Optimization-Based Feature Weight Learning (OFWL) framework in which feature weights are updated during training using validation feedback instead of being fixed in advance. A key idea is that weight updates are accepted only when they improve validation loss, helping the model focus on generalization rather than simply fitting the training data. The learned feature representation is then used in a hybrid model that combines Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Feature stability is also examined through variance analysis, and statistical testing is used to check the consistency of the results. The method is evaluated on the KDDCup99 dataset and shows better performance than baseline models while keeping the computational cost low.
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