Enhancing Intrusion Detection Systems with Hybrid HHO-WOA Optimization and GBM Classifier

Abstract

In this paper, we propose a hybrid Intrusion Detection System (IDS) that leverages Harris Hawks Optimization (HHO) and Whale Optimization Algorithm (WOA) for feature selection to enhance the detection of cyberattacks. The hybrid approach reduces the dimensionality of the NSL-KDD dataset, allowing the IDS to operate more efficiently. The reduced feature set is then classified using Logistic Regression (LR) and Gradient Boosting Machine (GBM) classifiers. Performance evaluation demonstrates that the GBM-HHO/WOA combination outperforms the LR-HHO/WOA approach, achieving an accuracy of 97.59%. These results indicate that integrating HHO and WOA significantly improves the IDS's ability to identify intrusions while maintaining high computational efficiency. This research highlights the potential of advanced optimization techniques to strengthen network security against evolving threats.