Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

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aut.relation.endpage16
aut.relation.journalJournal of Computer Networks and Communicationsen_NZ
aut.relation.startpage1
aut.researcherHutcheson, Catherine
dc.contributor.authorOgwara, NOen_NZ
dc.contributor.authorPetrova, Ken_NZ
dc.contributor.authorYang, MLen_NZ
dc.date.accessioned2022-03-06T23:27:42Z
dc.date.available2022-03-06T23:27:42Z
dc.date.copyright2022en_NZ
dc.date.issued2022en_NZ
dc.description.abstractAttacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed in this study (CCAID: cloud computing, attack, and intrusion detection) aims to improve the performance of intrusion detection systems (IDS) operating in CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). The selected features used to train the ML (machine learning) model of the intrusion detection component comprised a binary detection engine for the identification of malicious/attack packets and a multiclassification detection engine for the identification of the type of attack. Both detection engines deploy ensemble classifiers. Experiments were carried out using the NSL KDD dataset. The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). These results compare very favourably with the results reported in the literature and indicate the feasibility of the framework implementation.en_NZ
dc.identifier.citationJournal of Computer Networks and Communications, Volume 2022, Article ID 5988567, 16 pages, https://doi.org/10.1155/2022/5988567
dc.identifier.doi10.1155/2022/5988567en_NZ
dc.identifier.issn2090-7141en_NZ
dc.identifier.issn2090-715Xen_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14975
dc.languageenen_NZ
dc.publisherHindawi Limiteden_NZ
dc.relation.urihttps://www.hindawi.com/journals/jcnc/2022/5988567/
dc.rightsCopyright © 2022 Noah Oghenefego Ogwara et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.accessrightsOpenAccessen_NZ
dc.titleTowards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approachen_NZ
dc.typeJournal Article
pubs.elements-id450218
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences/Network Security Research Group
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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