HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System-Reference-Cited by-同舟云学术

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

Published:2017 Issue:8 Volume:E100.D Page:1729-1737
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

TAMA Bayu Adhi¹²,RHEE Kyung-Hyune²

Affiliation:

1. Faculty of Computer Science, University of Sriwijaya

2. Laboratory of Information Security and Internet Applications (LISIA), Dept. of IT Convergence and Application Engineering, Pukyong National University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E100.D/8/E100.D_2016ICP0018/_pdf

Reference36 articles.

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2. [2] B.A. Tama and K.H. Rhee, “Data mining techniques in DoS/DDoS attack detection: A literature review,” Information (Japan), vol.18, no.8, p.3739, 2015.

3. [3] X.-S. Gan, J.-S. Duanmu, J.-F. Wang, and W. Cong, “Anomaly intrusion detection based on PLS feature extraction and core vector machine,” Knowledge-Based Systems, vol.40, pp.1-6, 2013. 10.1016/j.knosys.2012.09.004

4. [4] B.A. Tama and K.H. Rhee, “A combination of PSO-based feature selection and tree-based classifiers ensemble for intrusion detection systems,” in Advances in Computer Science and Ubiquitous Computing, vol.373, pp.489-495, Springer, 2015. 10.1007/978-981-10-0281-6_71

5. [5] S. Mukkamala, A.H. Sung, and A. Abraham, “Intrusion detection using an ensemble of intelligent paradigms,” J. Netw. Comput. Appl., vol.28, no.2, pp.167-182, 2005. 10.1016/j.jnca.2004.01.003

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