Traffic Similarity Observation Using a Genetic Algorithm and Clustering

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dc.contributor.authorOujezský, Václavcs
dc.contributor.authorHorváth, Tomášcs
dc.coverage.issue4cs
dc.coverage.volume6cs
dc.date.issued2018-11-11cs
dc.description.abstractThis article presents a technique of traffic similarity observation based on the statistical method of survival analysis by using a genetic algorithm. The basis comes from the k-means clustering algorithm. The observed traffic is collected from different network sources by using a NetFlow collector. The purpose of this technique is to propose a process of finding spread malicious traffic, e.g., ransomware, and considers the possibility of implementing a genetic-based algorithm. In our solution, a chromosome is created from clustering k-means centers, and the Davies–Bouldin validity index is used as the second fitness value in the solution.en
dc.formattextcs
dc.format.extent1-10cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationTechnologies - MDPI. 2018, vol. 6, issue 4, p. 1-10.en
dc.identifier.doi10.3390/technologies6040103cs
dc.identifier.issn2227-7080cs
dc.identifier.orcid0000-0001-7629-6299cs
dc.identifier.orcid0000-0001-8659-8645cs
dc.identifier.other138952cs
dc.identifier.researcheridQ-9784-2017cs
dc.identifier.scopus57160133400cs
dc.identifier.urihttp://hdl.handle.net/11012/137212
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofTechnologies - MDPIcs
dc.relation.urihttps://www.mdpi.com/2227-7080/6/4/103cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2227-7080/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectClustering algorithmsen
dc.subjectEvolutionary computationen
dc.subjectIP networksen
dc.subjectInformation securityen
dc.subjectProgramming.en
dc.titleTraffic Similarity Observation Using a Genetic Algorithm and Clusteringen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-138952en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:41:59en
sync.item.modts2025.01.17 15:19:36en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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