Research on Feature Selection Method of Intrusion Detection Based on Deep Belief Network
- 10.2991/icmmita-15.2015.107How to use a DOI?
- Feature Selection;Deep Belief Network ;Intrusion Detection
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.
- © 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/).
Cite this article
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 -