Publication:
Identifying False Alarm Rates for Intrusion Detection System with Data Mining

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dc.contributor.authorSabri, FNMen_US
dc.contributor.authorNorwawi, NMen_US
dc.contributor.authorSeman, Ken_US
dc.date.accessioned2024-05-29T03:26:40Z
dc.date.available2024-05-29T03:26:40Z
dc.date.issued2011
dc.description.abstractIntrusion Detection Systems (IDS) are very important in determining how secure a system is, and to discover several types of attack such as Denial of Service (DOS), Probes and User to Root (U2R) attacks. However, recently false alarm rates and accuracy of detection are happens to be the most important issues and challenges in designing effective IDS. Therefore, this study is aimed at detecting denial of services attack and normal traffic using Knowledge Discovery and Data Mining Cup 99(KDD CUP 99) dataset to reduce the false alarm rates. Data mining is used to extract the useful information from large databases. The results have shown that the data mining technique reduces the false alarm rates and increase the accuracy of the system.en_US
dc.identifier.epage99
dc.identifier.issn1738-7906
dc.identifier.issue4
dc.identifier.scopusWOS:000217333300014
dc.identifier.spage95
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/12142
dc.identifier.volume11
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherInt Journal Computer Science & Network Security-Ijcsnsen_US
dc.relation.ispartofInternational Journal Of Computer Science And Network Securityen_US
dc.sourceWeb Of Science (ISI)
dc.subjectIntrusion detection systemen_US
dc.subjectaccuracyen_US
dc.subjectfalse alarm rateen_US
dc.subjectdata miningen_US
dc.subjectdenial of serviceen_US
dc.titleIdentifying False Alarm Rates for Intrusion Detection System with Data Miningen_US
dc.typeArticleen_US
dspace.entity.typePublication

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