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As the number of networked computers grows, intrusion detection becomes an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents the work to test and improve the performance of an intrusion detection system based on C-fuzzy decision tree, a new class of decision tree. The tree grows gradually by using Fuzzy C-means clustering (FCM) algorithm to split the patterns in a selected node with the maximum heterogeneity into C corresponding children nodes.
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