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AuthorEltanbouly, Sohaila
AuthorBashendy, May
AuthorAlnaimi, Noora
AuthorChkirbene, Zina
AuthorErbad, Aiman
Available date2024-03-04T04:55:29Z
Publication Date2020-02-01
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
Identifierhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089465
CitationS. Eltanbouly, M. Bashendy, N. AlNaimi, Z. Chkirbene and A. Erbad, "Machine Learning Techniques for Network Anomaly Detection: A Survey," 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar, 2020, pp. 156-162, doi: 10.1109/ICIoT48696.2020.9089465.
ISBN9781728148212
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85085496336&origin=inward
URIhttp://hdl.handle.net/10576/52616
AbstractNowadays, distributed data processing in cloud computing has gained increasing attention from many researchers. The intense transfer of data has made the network an attractive and vulnerable target for attackers to exploit and experiment with different types of attacks. Therefore, many intrusion detection techniques have been evolving to protect cloud distributed services by detecting the different attack types on the network. Machine learning techniques have been heavily applied in intrusion detection systems with different algorithms. This paper surveys recent research advances linked to machine learning techniques. We review some representative algorithms and discuss their proprieties in detail. We compare them in terms of intrusion accuracy and detection rate using different data sets.
Languageen
PublisherIEEE
SubjectAnomaly detection
intrusion detection systems
machine Learning
network security
TitleMachine Learning Techniques for Network Anomaly Detection: A Survey
TypeConference Paper
Pagination156-162
dc.accessType Abstract Only


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