Hybrid Machine Learning for Network Anomaly Intrusion Detection
| Author | Chkirbene, Zina |
| Author | Eltanbouly, Sohaila |
| Author | Bashendy, May |
| Author | Alnaimi, Noora |
| Author | Erbad, Aiman |
| Available date | 2024-03-04T04:51:02Z |
| Publication Date | 2020-02-01 |
| Publication Name | 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 |
| Identifier | http://dx.doi.org/10.1109/ICIoT48696.2020.9089575 |
| Citation | Z. Chkirbene, S. Eltanbouly, M. Bashendy, N. AlNaimi and A. Erbad, "Hybrid Machine Learning for Network Anomaly Intrusion Detection," 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar, 2020, pp. 163-170, doi: 10.1109/ICIoT48696.2020.9089575. |
| ISBN | 9781728148212 |
| 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. |
| Language | en |
| Publisher | IEEE |
| Subject | Anomaly detection intrusion detection systems machine Learning network security |
| Type | Conference Paper |
| Pagination | 163-170 |
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