Feature Selection and Deep Learning based Approach for Network Intrusion Detection
Jie Ling, Chengzhi Wu
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.122How to use a DOI?
- Intrusion detection, Random forest, Deep learning, Feature selection.
- 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.
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 -