Improved C-Fuzzy Decision Tree with controllable membership Characteristics for Intrusion Detection
Krishnamoorthi Makkithaya, N. V. Subba Reddy, U. Dinesh Acharya · Artificial Intelligence and Pattern Recognition · 2008
As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with eash one has its own merits and demerits. This paper presents an improved c-fuzzy desisio tree with controllable membership characteristics for intrusion detection.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. We use a modified fuzzy C-means algorithm with an extended distance measure to include an additional higher order tern, as defined in. We also used a hybrid model to select suitable intial points for the FCM. Experimental results have shown that our improved version performs beter resulting in an effective intrusion detection system.