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dc.contributor.authorJadidi, Zahra
dc.contributor.authorMuthukkumarasamy, Vallipuram
dc.contributor.authorSithirasenan, Elankayer
dc.contributor.authorSingh, Kalvinder
dc.date.accessioned2019-04-15T04:48:14Z
dc.date.available2019-04-15T04:48:14Z
dc.date.issued2016
dc.identifier.issn1796-2056
dc.identifier.doi10.4304/jnw.11.1.16-27
dc.identifier.urihttp://hdl.handle.net/10072/99624
dc.description.abstractModern Internet has enabled wider usage, resulting in increased network traffic. Due to the high volume of data packets in networking, sampling techniques are widely used in flow-based network management software to manage traffic load. However, sampling processes reduce the likelihood of anomaly detection. Many studies have been carried out at improving the accuracy of anomaly detection. However, only a few studies have considered it with sampled flow traffic. In our study, we investigate the use of an artificial neural network (ANN)based classifier to improve the accuracy of flow-based anomaly detection in sampled traffic. A feedback from the ANN-based anomaly detector determines the type of the flow sampling method that should be used. Our proposed technique handles malicious flows and benign flows with different sampling methods. To evaluate the proposed sampling technique, a number of flow-based datasets are generated. Our experiments confirm that the proposed technique improves the percentage of the sampled malicious flows by about 7% and it can preserve the majority of traffic information.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAcademy Publisher
dc.relation.ispartofpagefrom16
dc.relation.ispartofpageto27
dc.relation.ispartofissue1
dc.relation.ispartofjournalJournal of Networks
dc.relation.ispartofvolume11
dc.subject.fieldofresearchNeural, Evolutionary and Fuzzy Computation
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode080108
dc.subject.fieldofresearchcode0801
dc.titleIntelligent Sampling Using an Optimized Neural Network
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionVersion of Record (VoR)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2016 Academy Publisher. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorMuthukkumarasamy, Vallipuram
gro.griffith.authorSithirasenan, Elankayer
gro.griffith.authorSingh, Kalvinder
gro.griffith.authorJadidi, Zahra


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