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<p>The rapid increase in network traffic has created an urgent need for more effective security systems. Intrusion Detection Systems (IDS) are essential for network security but struggle with the growing volume and complexity of data, particularly in efficiently processing high-dimensional network traffic data. We introduce AEJaya+DE, a novel optimization approach that combines the Adaptive Enhanced Jaya Algorithm (AEJaya) with Differential Evolution (DE) to address these challenges. AEJaya+DE addresses the limitations of the basic Jaya algorithm, which often gets trapped in local optima due to its single learning strategy. Our method enhances the algorithm’s performance through two key innovations: it improves local search capabilities using local attractors for exploitation, and it strengthens global exploration through historical population data. The algorithm incorporates dynamic parameter adjustment and adaptive probabilistic strategy selection to maintain an optimal balance between the exploration and exploitation phases. We validated AEJaya+DE’s effectiveness using two standard benchmark datasets. On the UNSW-NB15 dataset, it reduced the feature set from 42 to 21 features while achieving 96.10% accuracy using the XGBoost classifier. The NSL-KDD dataset decreased features from 41 to 24 while reaching 99.93% accuracy with the CatBoost classifier. These results demonstrate AEJaya+DE’s ability to select a compact yet highly informative feature set, significantly improving intrusion detection systems’ accuracy and computational efficiency. The method represents a significant advancement in network security, offering a robust and efficient solution for intrusion detection across various network environments.</p>

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AEJaya+DE: a hybrid feature selection method for machine learning-based network intrusion detection on cybersecurity datasets

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Fawzia Omer Albasheer,
  • Ezhil Kalaimannan,
  • Mayank Agarwal

摘要

The rapid increase in network traffic has created an urgent need for more effective security systems. Intrusion Detection Systems (IDS) are essential for network security but struggle with the growing volume and complexity of data, particularly in efficiently processing high-dimensional network traffic data. We introduce AEJaya+DE, a novel optimization approach that combines the Adaptive Enhanced Jaya Algorithm (AEJaya) with Differential Evolution (DE) to address these challenges. AEJaya+DE addresses the limitations of the basic Jaya algorithm, which often gets trapped in local optima due to its single learning strategy. Our method enhances the algorithm’s performance through two key innovations: it improves local search capabilities using local attractors for exploitation, and it strengthens global exploration through historical population data. The algorithm incorporates dynamic parameter adjustment and adaptive probabilistic strategy selection to maintain an optimal balance between the exploration and exploitation phases. We validated AEJaya+DE’s effectiveness using two standard benchmark datasets. On the UNSW-NB15 dataset, it reduced the feature set from 42 to 21 features while achieving 96.10% accuracy using the XGBoost classifier. The NSL-KDD dataset decreased features from 41 to 24 while reaching 99.93% accuracy with the CatBoost classifier. These results demonstrate AEJaya+DE’s ability to select a compact yet highly informative feature set, significantly improving intrusion detection systems’ accuracy and computational efficiency. The method represents a significant advancement in network security, offering a robust and efficient solution for intrusion detection across various network environments.