IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Special Section on Information and Communication System Security
HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System
Bayu Adhi TAMAKyung-Hyune RHEE
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2017 Volume E100.D Issue 8 Pages 1729-1737

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Abstract

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.

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© 2017 The Institute of Electronics, Information and Communication Engineers
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