Publication:
STAKCERT worm relational model for worm detection

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dc.Conferencecode85243
dc.Conferencedate30 June 2010 through 2 July 2010
dc.ConferencelocationLondon
dc.ConferencenameWorld Congress on Engineering 2010, WCE 2010
dc.citedby1
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.affiliationsUniversity of Bradford
dc.contributor.authorSaudi M.M.en_US
dc.contributor.authorCullen A.J.en_US
dc.contributor.authorWoodward M.E.en_US
dc.date.accessioned2024-05-29T01:55:46Z
dc.date.available2024-05-29T01:55:46Z
dc.date.issued2010
dc.description.abstractIn this paper, a new STAKCERT worm relational model is being developed based on the evaluation of the STAKCERT worm classification using the dynamic, static and statistical analysis. A case study was conducted to evaluate the effectiveness of this STAKCERT relational model. The case study result analysis showed that the 5 main features in the relational model play an important role in identifying the vulnerability exploited, the damage caused, the expected rate of worm propagation, the chronological flows and the detection avoidance techniques used by the worms. As such, perhaps this new relational model produced can be used as the basis for organizations and end users in detecting worm incidents.
dc.description.natureFinalen_US
dc.description.sponsorshipIAENG Society of Artificial Intelligence
dc.description.sponsorshipIAENG Society of Bioinformatics
dc.description.sponsorshipIAENG Society of Computer Science
dc.description.sponsorshipIAENG Society of Data Mining
dc.description.sponsorshipIAENG Society of Electrical Engineering
dc.identifier.epage473
dc.identifier.isbn9789880000000
dc.identifier.scopus2-s2.0-79959875236
dc.identifier.spage469
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79959875236&partnerID=40&md5=2e7fa4137fba91eb904844e2f57fa881
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9752
dc.identifier.volume1
dc.languageEnglish
dc.language.isoen_US
dc.relation.ispartofWCE 2010 - World Congress on Engineering 2010
dc.sourceScopus
dc.subjectDynamic analysisen_US
dc.subjectRelational modelen_US
dc.subjectStatic analysis and statistical analysisen_US
dc.subjectEnd usersen_US
dc.subjectRelational Modelen_US
dc.subjectResult analysisen_US
dc.subjectTechniques useden_US
dc.subjectWorm detectionen_US
dc.subjectWorm propagationen_US
dc.subjectDamage detectionen_US
dc.subjectDynamic analysisen_US
dc.subjectStatistical methodsen_US
dc.titleSTAKCERT worm relational model for worm detection
dc.typeConference Paperen_US
dspace.entity.typePublication

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