Research Article
Two-Level Feature Selection Method for Low Detection Rate Attacks in Intrusion Detection
@INPROCEEDINGS{10.1007/978-3-030-21373-2_58, author={Chundong Wang and Xin Ye and Xiaonan He and Yunkun Tian and Liangyi Gong}, title={Two-Level Feature Selection Method for Low Detection Rate Attacks in Intrusion Detection}, proceedings={Security and Privacy in New Computing Environments. Second EAI International Conference, SPNCE 2019, Tianjin, China, April 13--14, 2019, Proceedings}, proceedings_a={SPNCE}, year={2019}, month={6}, keywords={Feature selection Information gain mRMR Intrusion detection}, doi={10.1007/978-3-030-21373-2_58} }- Chundong Wang
Xin Ye
Xiaonan He
Yunkun Tian
Liangyi Gong
Year: 2019
Two-Level Feature Selection Method for Low Detection Rate Attacks in Intrusion Detection
SPNCE
Springer
DOI: 10.1007/978-3-030-21373-2_58
Abstract
In view of the fact that some attacks have low detection rates in intrusion detection dataset, a two-level feature selection method based on minimal-redundancy-maximal-relevance (mRMR) and information gain (IG) was proposed. In this method, irrelevant and redundant features were filtered preliminarily to reduce data dimension by using mRMR algorithm, and highly correlated features to low detection rate attacks were obtained based on the calculation of information gain, and finally these features were integrated together to get final feature subset. The experimental results showed that the classification result of the feature subset filtered by this method had a better classification performance than the current filtering methods and improved the testing results of some attacks with low detection rates effectively.
