Improved C-fuzzy decision tree with controllable membership characteristics for intrusion detection

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 each one has its own merits and demerits. This paper presents an improved c-fuzzy decision 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 term, as defined in. We also used a hybrid model to select suitable initial points for the FCM. Experimental results have shown that our improved version performs better resulting in an effective intrusion detection system.