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dc.contributor.authorKiruki, Jane K.
dc.contributor.authorMuketha, Geoffrey M.
dc.contributor.authorKamau, Gabriel
dc.date.accessioned2024-04-23T08:38:36Z
dc.date.available2024-04-23T08:38:36Z
dc.date.issued2023-05
dc.identifier.citationInternational Journal of Network Security & Its Applications (IJNSA) Vol.15, No.3, May 2023en_US
dc.identifier.urichrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://aircconline.com/ijnsa/V15N3/15323ijnsa03.pdf
dc.identifier.urihttps://typeset.io/papers/a-novel-alert-correlation-technique-for-filtering-network-30dhpusb
dc.identifier.urihttps://www.youtube.com/watch?v=bNiMG-JqhWc
dc.identifier.urihttp://repository.mut.ac.ke:8080/xmlui/handle/123456789/6434
dc.identifier.urihttps://doi.org/10.5121/ijnsa.2023.15303
dc.description.abstractAn alert correlation is a high-level alert evaluation technique for managing large volumes of irrelevant and redundant intrusion alerts raised by Intrusion Detection Systems (IDSs).Recent trends show that pure intrusion detection no longer can satisfy the security needs of organizations. One problem with existing alert correlation techniques is that they group related alerts together without putting their severity into consideration. This paper proposes a novel alert correlation technique that can filter unnecessary and low impact alerts from a large volume of intrusion. The proposed technique is based on a supervised feature selection method that uses class type to define the correlation between alerts. Alerts of similar class type are identified using a class label. Class types are further classified based on their metric ranks of low, medium and high level. Findings show that the technique is able detect and report high level intrusions.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Network Security & Its Applicationsen_US
dc.subjectAlert correlation, merging, classification, alert clustering, alert prioritization, pre-processingen_US
dc.titleA Novel Alert Correlation Technique for Filtering Network Attacksen_US
dc.typeArticleen_US


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