Feature Selection and Deep Learning based Approach for Network Intrusion Detection

DOI

10.2991/icmeit-19.2019.122How to use a DOI?

Keywords

Intrusion detection, Random forest, Deep learning, Feature selection.

Abstract

The intrusion detection system deals with huge amount of data containing redundant and noisy features and the poor classifier algorithm causing the degradation of detection accuracy, in this paper, we introduce the random forest feature selection algorithm and propose a method that multi-classifier ensemble based on deep learning for intrusion detection. It used the random forest feature selection algorithm to extract optimal feature subset that are used to train by support vector machine, decision tree, naïve bayes and k-nearest neighbor classification algorithm, then, applying the deep learning to stack the output of four classifiers. The experimental results show that the proposed method can effectively improve the accuracy of intrusion detection compared with the majoring voting algorithm.

Copyright

© 2019, 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  - Jie Ling
AU  - Chengzhi Wu
PY  - 2019/04
DA  - 2019/04
TI  - Feature Selection and Deep Learning based Approach for Network Intrusion Detection
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 764
EP  - 770
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.122
DO  - 10.2991/icmeit-19.2019.122
ID  - Ling2019/04
ER  -