HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System

DOI Web Site 被引用文献1件 参考文献27件
  • TAMA Bayu Adhi
    Faculty of Computer Science, University of Sriwijaya Laboratory of Information Security and Internet Applications (LISIA), Dept. of IT Convergence and Application Engineering, Pukyong National University
  • RHEE Kyung-Hyune
    Laboratory of Information Security and Internet Applications (LISIA), Dept. of IT Convergence and Application Engineering, Pukyong National University

説明

<p>Anomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detection approach by hybridizing three techniques, i.e. particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) for feature selection and ensemble of four tree-based classifiers, i.e. random forest (RF), naive bayes tree (NBT), logistic model trees (LMT), and reduces error pruning tree (REPT) for classification. Proposed approach is implemented on NSL-KDD dataset and from the experimental result, it significantly outperforms the existing methods in terms of accuracy and false alarm rate.</p>

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