@inproceedings{7b99be082d5d4e6d8e9446d58bb759f3,
title = "Hybrid Machine Learning for Network Anomaly Intrusion Detection",
abstract = "In this paper, a hybrid approach of combing two machine learning algorithms is proposed to detect the different possible attacks by performing effective feature selection and classification. This system uses Random Forest algorithm for the feature selection to find the most important features combined with Classification and Regression Trees (CART) for the classification of the different attack classes. The proposed system was tested using the UNSW-NB15 dataset and the results show that the proposed method achieves a good performance compared with the existing algorithms.",
keywords = "Anomaly detection, intrusion detection systems, machine Learning, network security",
author = "Zina Chkirbene and Sohaila Eltanbouly and May Bashendy and Noora Alnaimi and Aiman Erbad",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 ; Conference date: 02-02-2020 Through 05-02-2020",
year = "2020",
month = feb,
doi = "10.1109/ICIoT48696.2020.9089575",
language = "English",
series = "2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "163--170",
booktitle = "2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020",
address = "United States",
}