Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/633
Full metadata record
DC FieldValueLanguage
dc.contributor.authorOlalere, Morufu-
dc.contributor.authorYisa, Rhoda Nnaba-
dc.contributor.authorOjeniyi, Joseph A-
dc.contributor.authorNwaocha, Vivian O.-
dc.date.accessioned2021-06-01T11:29:20Z-
dc.date.available2021-06-01T11:29:20Z-
dc.date.issued2020-05-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/633-
dc.language.isoenen_US
dc.relation.ispartofseriesVol. 9 No. 1&2;-
dc.subjectHTTP Botneten_US
dc.subjectmachine learningen_US
dc.subjectClassifiersen_US
dc.subjectRandom Foresten_US
dc.subjectBotmasteren_US
dc.titleIdentification of Best Machine Learning Algorithms for Detection of HTTP Botnet Attacken_US
dc.typeArticleen_US
Appears in Collections:Cyber Security Science

Files in This Item:
File Description SizeFormat 
olalere et al 2020_identification of best ML.pdf3.29 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.