@inproceedings{db9387d5518143e0b13d88537fa6e883,
title = "Pearson Correlation Attribute Evaluation-based Feature Selection for Intrusion Detection System",
abstract = "IDS helps to overcome the network attack by taking appropriate preventive measures. The data mining method has good adaptability to new attack types; however, it consumes much time for high dimensional data. Therefore, the system needs a reduction of that high dimension. In this paper, we use a correlation approach of the attribute to evaluate those high dimensional data. To achieve a better environment, we propose a cut-off value of correlation to select some best features to use in the classification process. The best cut-off value in our experiment is 0.2 in RF classification that reaches 99.36% accuracy. The selection feature can reduce the time consumed in the running system.",
keywords = "Pearson's correlation, feature, intrusion detection system, network security, selection",
author = "Yuna Sugianela and Tohari Ahmad",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Smart Technology and Applications, ICoSTA 2020 ; Conference date: 20-02-2020",
year = "2020",
month = feb,
doi = "10.1109/ICoSTA48221.2020.1570613717",
language = "English",
series = "Proceeding - ICoSTA 2020: 2020 International Conference on Smart Technology and Applications: Empowering Industrial IoT by Implementing Green Technology for Sustainable Development",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceeding - ICoSTA 2020",
address = "United States",
}
