Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Feature Selection and Deep Learning based Approach for Network Intrusion Detection

Authors
Jie Ling, Chengzhi Wu
Corresponding Author
Chengzhi Wu
Available Online April 2019.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.122How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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
PB  - Atlantis Press
SP  - 764
EP  - 770
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.122
DO  - https://doi.org/10.2991/icmeit-19.2019.122
ID  - Ling2019/04
ER  -