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<p>The rising complexity of cyber threats has underscored the critical need for intelligent Intrusion Detection Systems (IDS) that can efficiently handle high-dimensional data and evolving attack patterns. Traditional IDS often suffer from redundancy and inefficiencies. This research integrating Support Vector Machine (SVM) and C4.5 decision tree models with metaheuristic optimized machine learning framework for intrusion detection, utilizing the NSL-KDD and UNSW-NB15 datasets. The proposed methodology involves comprehensive data preprocessing, discriminative feature extraction, and hybrid model development using various metaheuristic algorithms, including particle swarm optimization (PSO), modified PSO, BAT, Cuckoo Search (CS), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and Genetic Algorithm (GA). These algorithms optimize the selection of relevant features to enhance detection accuracy and reduce false positives. Experimental analysis reveals that hybrid models particularly MPSO-C45, CS-C45, BAT-SVM, and MPSO-SVM significantly outperform traditional classifiers in both datasets. On NSL-KDD, MPSO-C45 and CS-C45 achieved near-perfect metrics F-measure of 0.993, sensitivity of 0.994 with low runtime of 1.7&#xa0;s. While on UNSW-NB15, GWO-C45, CS-C45, and PSO-SVM achieved high classification results accuracy of 1.000, and MSE of 0. Comparative evaluation against existing state-of-the-art approaches confirms the superiority of the proposed models, particularly in terms of balancing accuracy, runtime, and false positive reduction. However, GA-based models consistently underperformed, indicating limitations in their optimization capability. Despite excellent results, the study acknowledges challenges in real-time deployment and computational overhead.</p>

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A hybrid approach: Meta-Heuristic with optimizing machine learning algorithms for intrusion detection systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月24日

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  • Abdullah Khan,
  • Dzati Athiar Ramli,
  • Muhammad Zubair Rehman,
  • Nazri Mohd Nawi

摘要

The rising complexity of cyber threats has underscored the critical need for intelligent Intrusion Detection Systems (IDS) that can efficiently handle high-dimensional data and evolving attack patterns. Traditional IDS often suffer from redundancy and inefficiencies. This research integrating Support Vector Machine (SVM) and C4.5 decision tree models with metaheuristic optimized machine learning framework for intrusion detection, utilizing the NSL-KDD and UNSW-NB15 datasets. The proposed methodology involves comprehensive data preprocessing, discriminative feature extraction, and hybrid model development using various metaheuristic algorithms, including particle swarm optimization (PSO), modified PSO, BAT, Cuckoo Search (CS), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), and Genetic Algorithm (GA). These algorithms optimize the selection of relevant features to enhance detection accuracy and reduce false positives. Experimental analysis reveals that hybrid models particularly MPSO-C45, CS-C45, BAT-SVM, and MPSO-SVM significantly outperform traditional classifiers in both datasets. On NSL-KDD, MPSO-C45 and CS-C45 achieved near-perfect metrics F-measure of 0.993, sensitivity of 0.994 with low runtime of 1.7 s. While on UNSW-NB15, GWO-C45, CS-C45, and PSO-SVM achieved high classification results accuracy of 1.000, and MSE of 0. Comparative evaluation against existing state-of-the-art approaches confirms the superiority of the proposed models, particularly in terms of balancing accuracy, runtime, and false positive reduction. However, GA-based models consistently underperformed, indicating limitations in their optimization capability. Despite excellent results, the study acknowledges challenges in real-time deployment and computational overhead.