Integrating genetic algorithms and fuzzy c-means for anomaly detection

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dc.contributor.authorWitcha Chimphlee
dc.contributor.authorAbdul Hanan Abdullah
dc.contributor.authorMohd Moor Md Sap
dc.contributor.authorSiriporn Chimphlee
dc.contributor.authorSurat Srinoy
dc.contributor.correspondenceW. Chimphlee; Faculty of Science and Technology, Suan Dusit Rajabhat University, Dusit, Bangkok, 295 Rajasrima Road, Thailand; email: witcha_chi@dusit.ac.th
dc.date.accessioned2025-03-10T07:38:08Z
dc.date.available2025-03-10T07:38:08Z
dc.date.issued2005
dc.description.abstractThe goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate. We used data set from 1999 KDD intrusion detection contest. © 2005 IEEE.
dc.identifier.citationProceedings of INDICON 2005: An International Conference of IEEE India Council
dc.identifier.doi10.1109/INDCON.2005.1590237
dc.identifier.isbn0780395034; 978-078039503-9
dc.identifier.scopus2-s2.0-33847128421
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5078
dc.languageEnglish
dc.rights.holderScopus
dc.subjectAnomaly detection
dc.subjectFuzzy c-means
dc.subjectGenetic algorithms
dc.subjectUnsupervised clustering
dc.titleIntegrating genetic algorithms and fuzzy c-means for anomaly detection
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-33847128421&doi=10.1109%2fINDCON.2005.1590237&partnerID=40&md5=0cf8551734c434f7cdd514f63eb3e645
oaire.citation.endPage579
oaire.citation.startPage575
oaire.citation.volume2005
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