Efficient Hybrid Feature Engineering and Supervised Learning Approach for Network Traffic Classification in Intrusion Detection Systems
International journal of intelligent engineering and systems · 2025
Conventional feature selection methods frequently face difficulties in identifying the most significant features within high-dimensional data, which can result in lower classification accuracy and heightened computational demands.These methods typically do not consider the internal structure or harmony within the data, which limits their ability to identify optimal subsets of features.In this study, we address these limitations by proposing an efficient feature selection method, BER-WOA (Bayesian Expectile Regression -Whale Optimization Algorithm), for improving the performance of intrusion detection systems.The proposed approach consists of two stages: the first stage uses Bayesian Expectile Regression (BER) to select optimal features, reducing the search space.In the second stage, the Whale Optimization Algorithm (WOA) is applied to search within the smaller feature subset to identify the most relevant features for classification.The method is applied to two widely used datasets, NSL-KDD and UNSW-NB15, and evaluated using four popular classifiers: SVM, KNN, RF, and DT.The proposed BER-WOA method effectively reduces the number of features while maintaining high classification accuracy.On the NSL-KDD dataset, the method selected 18 features, achieving an accuracy of 99.14% with RF, while on the UNSW-NB15 dataset, 21 features were selected, yielding an accuracy of 99.72% with RF.BER-WOA method achieved even better results, with a 46.84% reduction in execution time for UNSW-NB15 and a 47.06% reduction for NSL-KDD.The results highlight that the BER-WOA method outperforms other feature selection techniques in terms of both classification performance and computational time.The proposed approach is shown to be a promising solution for efficient feature selection in intrusion detection systems, offering enhanced accuracy and reduced complexity.