A combined feature selection method based on clustering in intrusion detection

DOI

10.2991/amcce-17.2017.11How to use a DOI?

Keywords

Feature selection, Relief algorism, k-means clustering, Relief+k-means.

Abstract

The rapid development of information technology generates high dimension and large scale data, which puts severer challenges to network security. Feature selection is proposed for the reduction of data dimension so that features of original data are utmostly retained and improving the effectiveness of data processing. This paper proposes a new method of feature extraction by combining two algorithms. Firstly, removes some noise and irrelevant features after researching for correlation between features and categories; Secondly, furtherly optimize subsets to select key features through feature selection algorithm, whose Evaluate Function is based on clustering algorithm. KDDCUP9910% datasets is used to testify the experiment, whose result shows the method guarantees effective detection rate and reducing the data dimension effectively at the same time , the effectiveness of data detection is improved.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ting Huang
AU  - Wenbo Chen
AU  - RuisHeng Zhang
PY  - 2017/03
DA  - 2017/03
TI  - A combined feature selection method based on clustering in intrusion detection
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 65
EP  - 73
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.11
DO  - 10.2991/amcce-17.2017.11
ID  - Huang2017/03
ER  -