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Benchmarking bioinspired machine learning algorithms with CSE-CIC-IDS2018 network intrusions dataset

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dc.contributor.authorFerreira, Paulo
dc.contributor.authorAntunes, Mário
dc.date.accessioned2021-08-13T11:15:34Z
dc.date.available2021-08-13T11:15:34Z
dc.date.issued2020
dc.description.abstractThis paper aims to evaluate CSE-CIC-IDS2018 network intrusions dataset and benchmark a set of supervised bioinspired machine learning algo rithms, namely CLONALG Artificial Immune System, Learning Vector Quantization (LVQ) and Back-Propagation Multi-Layer Perceptron (MLP). The results obtained were also compared with an ensemble strategy based on a majority voting algorithm. The results obtained show the appropri ateness of using the dataset to test behaviour based network intrusion de tection algorithms and the efficiency of MLP algorithm to detect zero-day attacks, when comparing with CLONALG and LVQ.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPaulo Ferreira, Mário Antunes; "Benchmarking bioinspired machine learning algorithms with CSE-CIC-IDS2018 network intrusions dataset"; 26th edition of the Portuguese Conference on Pattern Recognition (RecPad'20); October 2020.pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.8/6105
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.titleBenchmarking bioinspired machine learning algorithms with CSE-CIC-IDS2018 network intrusions datasetpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.titlePortuguese Conference on Pattern Recognition (RecPad'20)pt_PT
person.familyNameAntunes
person.givenNameMário
person.identifierR-000-NX4
person.identifier.ciencia-idAF10-7EDD-5153
person.identifier.orcid0000-0003-3448-6726
person.identifier.scopus-author-id25930820200
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicatione3e87fb0-d1d6-44c3-985d-920a5560f8c1
relation.isAuthorOfPublication.latestForDiscoverye3e87fb0-d1d6-44c3-985d-920a5560f8c1

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