Show simple item record

AuthorChkirbene, Zina
AuthorEltanbouly, Sohaila
AuthorBashendy, May
AuthorAlnaimi, Noora
AuthorErbad, Aiman
Available date2024-03-04T04:51:02Z
Publication Date2020-02-01
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
Identifierhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089575
CitationZ. 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.
ISBN9781728148212
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85085510618&origin=inward
URIhttp://hdl.handle.net/10576/52615
AbstractIn 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.
Languageen
PublisherIEEE
SubjectAnomaly detection
intrusion detection systems
machine Learning
network security
TitleHybrid Machine Learning for Network Anomaly Intrusion Detection
TypeConference Paper
Pagination163-170
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record