Combination artificial ant clustering and K-PSO clustering approach to network security model

dc.contributor.authorSurat Srinoy
dc.contributor.authorWerasak Kurutach
dc.contributor.correspondenceS. Srinoy; Department of Computer Science, Suan Dusit Rajabhat University, Thailand; email: surat_sri@dusit.ac.th
dc.date.accessioned2025-03-10T07:38:08Z
dc.date.available2025-03-10T07:38:08Z
dc.date.issued2006
dc.description.abstractA Computer system now operate in an environment of near ubiquitous connectivity, whether tethered to an Ethernet cable or connected via wireless technology. While the availability of always on communication has created countless new opportunities for web based businesses, information sharing, and coordination, it has also created new opportunities for those that seek to illegally disrupt, subvert, or attack these activities. We present natural based data mining algorithm approach to data clustering. Artificial ant clustering algorithm is used to initially create raw clusters and then these clusters are refined using k-mean particle swarm optimization (KPSO). KPSO that has been developed as evolutionary-based clustering technique. The algorithm uses hybridization the k-means algorithm and PSO principle to find good partitions of the data. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose k-means particle swarm optimization clustering algorithm in the second stage for refinement mean of overcoming these complexities is proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999. © 2006 IEEE.
dc.identifier.citationProceedings - 2006 International Conference on Hybrid Information Technology, ICHIT 2006
dc.identifier.doi10.1109/ICHIT.2006.253601
dc.identifier.scopus2-s2.0-34247203682
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5073
dc.languageEnglish
dc.rights.holderScopus
dc.titleCombination artificial ant clustering and K-PSO clustering approach to network security model
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-34247203682&doi=10.1109%2fICHIT.2006.253601&partnerID=40&md5=2f213fa28fa221aa46345de4ed17836d
oaire.citation.endPage134
oaire.citation.startPage128
oaire.citation.volume2
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