An integrated fuzzy ants and artificial immune recognition system for anomaly detection

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dc.contributor.authorSurat Srinoy
dc.contributor.authorWerasak Kurutach
dc.contributor.correspondenceS. Srinoy; Department of Computer Science, Suan Dusit Rajabhat University, Bangkok, 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 intrusion is seen as any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. The introduction to networks and the internet caused great concern about the protection of sensitive information and have resulted in many computer security research efforts during the past few years. This paper highlights a novel approach for detecting intrusion based on bio-inspired algorithm. The intrusion detection model combines the fuzzy ants clustering algorithm and artificial immune recognition algorithm to maximize detection accuracy and minimize computational complexity. The implemented system has been tested on the training data set from DARPA DATA SET by MIT Lincoln Laboratory on intrusion. The applicability of the proposed method and the enhanced security it provides are discussed. © 2006 ICASE.
dc.identifier.citation2006 SICE-ICASE International Joint Conference
dc.identifier.doi10.1109/SICE.2006.315604
dc.identifier.isbn8995003855; 978-899500385-5
dc.identifier.scopus2-s2.0-34250731817
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5061
dc.languageEnglish
dc.rights.holderScopus
dc.subjectAnomaly detection
dc.subjectArtificial immune recognition system
dc.subjectFuzzy ants
dc.titleAn integrated fuzzy ants and artificial immune recognition system for anomaly detection
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-34250731817&doi=10.1109%2fSICE.2006.315604&partnerID=40&md5=6daf77b47963043e590e922abb8c08a6
oaire.citation.endPage5469
oaire.citation.startPage5464
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