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dc.contributor.authorMirza, Ali H.en_US
dc.coverage.spatialIzmir, Turkeyen_US
dc.date.accessioned2019-02-21T16:05:09Z
dc.date.available2019-02-21T16:05:09Z
dc.date.issued2018en_US
dc.identifier.isbn9781538615010
dc.identifier.urihttp://hdl.handle.net/11693/50235
dc.descriptionDate of Conference: 2-5 May 2018en_US
dc.description.abstractIn this paper, we execute anomaly detection over the computer networks using various machine learning algorithms. We then combine these algorithms to boost the overall performance. We implement three different types of classifiers, i.e, neural networks, decision trees and logistic regression. We then boost the overall performance of the intrusion detection algorithm using ensemble learning. In ensemble learning, we employ weighted majority voting scheme based on the individual classifier performance. We demonstrate a significant increase in the accuracy through a set of experiments KDD Cup 99 data set for computer network intrusion detection.
dc.language.isoEnglish
dc.source.title2018 26th Signal Processing and Communications Applications Conference (SIU)en_US
dc.relation.isversionofhttps://doi.org/10.1109/SIU.2018.8404704
dc.subjectAnomalyen_US
dc.subjectClassificationen_US
dc.subjectEnsembleen_US
dc.subjectNetwork intrusionen_US
dc.subjectOnline learningen_US
dc.titleComputer network intrusion detection using various classifiers and ensemble learningen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.identifier.doi10.1109/SIU.2018.8404704
dc.publisherIEEE


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