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

Bayu Adhi TAMA; Kyung-Hyune RHEE

doi:10.1587/transinf.2016ICP0018

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

Bayu Adhi TAMA, Kyung-Hyune RHEE

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.8 pp.1729-1737

Publication Date: 2017/08/01

Publicized: 2017/05/18

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016ICP0018

Type of Manuscript: Special Section PAPER (Special Section on Information and Communication System Security)

Category: Internet Security

Authors

Bayu Adhi TAMA
University of Sriwijaya,Pukyong National University
Kyung-Hyune RHEE
Pukyong National University

Keyword

classifier ensemble, intrusion detection systems, tree-based classifiers, hybrid feature selection

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Bayu Adhi TAMA, Kyung-Hyune RHEE, "HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 8, pp. 1729-1737, August 2017, doi: 10.1587/transinf.2016ICP0018.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016ICP0018/_p

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@ARTICLE{e100-d_8_1729,
author={Bayu Adhi TAMA, Kyung-Hyune RHEE, },
journal={IEICE TRANSACTIONS on Information},
title={HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System},
year={2017},
volume={E100-D},
number={8},
pages={1729-1737},
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.},
keywords={},
doi={10.1587/transinf.2016ICP0018},
ISSN={1745-1361},
month={August},}

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TY - JOUR
TI - HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System
T2 - IEICE TRANSACTIONS on Information
SP - 1729
EP - 1737
AU - Bayu Adhi TAMA
AU - Kyung-Hyune RHEE
PY - 2017
DO - 10.1587/transinf.2016ICP0018
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2017
AB - 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.
ER -