Research on Feature Selection Method of Intrusion Detection Based on Deep Belief Network

DOI

10.2991/icmmita-15.2015.107How to use a DOI?

Keywords

Feature Selection;Deep Belief Network ;Intrusion Detection

Abstract

Feature selection is one of the important factors that affect the intrusion detection system. Aiming at the problems due to selecting the high feature dimension and the redundancy causelow detection accuracy and high missing rate in the traditional intrusion detection system.In this paper, the deep belief network algorithm is given to select featureslayer by layer to reduce the feature dimension.As the deep belief network algorithm is an unsupervised learning algorithm, it is more suitable for selecting features from a large number of unlabeled data.Compared with other feature selection algorithm,the experiment shows the deep belief network algorithm is more effective than other algorithm in intrusion detection network.

Copyright

© 2015, 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  - Baoyi Wang
AU  - Shan Sun
AU  - Shaomin Zhang
PY  - 2015/11
DA  - 2015/11
TI  - Research on Feature Selection Method of Intrusion Detection Based on Deep Belief Network
BT  - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 556
EP  - 561
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-15.2015.107
DO  - 10.2991/icmmita-15.2015.107
ID  - Wang2015/11
ER  -