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dc.contributor.authorAhmed, Abdulghani Ali
dc.contributor.authorJabbar, Waheb A
dc.contributor.authorAl-Shakarchi, Ali
dc.contributor.authorPatel, Hiran
dc.date.accessioned2020-03-10T14:42:16Z
dc.date.available2020-03-10T14:42:16Z
dc.date.issued2020-03-09
dc.identifier.citationAhmed, A.A., Jabbar, W.A., Sadiq, A.S. and Patel, H. (2020) Deep learning-based classification model for botnet attack detection, Journal of Ambient Intelligence and Humanized Computing, https://doi.org/10.1007/s12652-020-01848-9en
dc.identifier.issn1868-5137en
dc.identifier.doi10.1007/s12652-020-01848-9en
dc.identifier.urihttp://hdl.handle.net/2436/623129
dc.description.abstractBotnets are vectors through which hackers can seize control of multiple systems and conduct malicious activities. Researchers have proposed multiple solutions to detect and identify botnets in real time. However, these proposed solutions have difficulties in keeping pace with the rapid evolution of botnets. This paper proposes a model for detecting botnets using deep learning to identify zero-day botnet attacks in real time. The proposed model is trained and evaluated on a CTU-13 dataset with multiple neural network designs and hidden layers. Results demonstrate that the deep-learning artificial neural network model can accurately and efficiently identify botnets.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.urlhttps://link.springer.com/article/10.1007/s12652-020-01848-9en
dc.subjectSecurityen
dc.subjectbotneten
dc.subjectFeed-forwarden
dc.subjectartificial neural networken
dc.subjectbackpropagationen
dc.subjectDeep Learningen
dc.titleDeep learning-based classification model for botnet attack detectionen
dc.typeJournal articleen
dc.identifier.journalJournal of Ambient Intelligence and Humanized Computingen
dc.date.updated2020-03-06T11:32:08Z
dc.date.accepted2020-02-26
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW10032020AAen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2021-03-09en
refterms.dateFCD2020-03-10T14:40:07Z
refterms.versionFCDAM


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