Surasit SongmaWitcha ChimphleeKiattisak MaichalernnukulParinya Sanguansat2025-03-102025-03-102012International Conference on ICT and Knowledge Engineering978-146732314-72157099X10.1109/ICTKE.2012.64085402-s2.0-84873376817https://repository.dusit.ac.th//handle/123456789/4975We propose a two-phase classification method. Specifically, in the first phase, a set of patterns (data) are clustered by the k-means algorithm. In the second phase, outliers are constructed by a distance-based technique and a class label is assigned to each pattern. The Knowledge Discovery Databases (KDD) Cup 1999 data set, which has been utilized extensively for development of intrusion detection systems, is used in our experiment. The results show that the proposed method is effective in intrusion detection. © 2012 IEEE.Classificationintrusion detectionk-meansKDD Cup 1999 data setoutlier detectionConference paperScopus