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Abstract

The rapid increase in internet usage, digital transformation, and the rise of interconnected devices have greatly expanded the attack surface, introducing new and evolving cybersecurity challenges. Conventional security solutions frequently have difficulty adjusting to complex threats and the vast dimensionality of network traffic data, particularly in the case of imbalanced datasets. To tackle these challenges, this research introduces a Hybrid Intrusion Detection System (HyIDS-EVO) that combines the Energy Valley Optimizer (EVO) for feature selection and dimensionality reduction with machine learning classifiers, which include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). The system’s effectiveness is assessed using the imbalanced NSL-KDD and CSE-CIC-IDS2018 datasets, employing downsampling to address class imbalance. EVO successfully condenses the feature set from 80 to 43 features for CSE-CIC-IDS2018 and from 42 to 19 for NSL-KDD, thereby improving classifier performance. Of the models evaluated, the DT-EVO combination yields the highest accuracy, achieving 99.78% on CSE-CIC-IDS2018 and 99.50% on NSL-KDD. In summary, the EVO-based HyIDS-EVO surpasses traditional methods in terms of accuracy, precision, recall, and F1-score, illustrating its strength and efficiency for contemporary intrusion detection in imbalanced network settings.

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