Intrusion Detection System using Modified C-Fuzzy Decision Tree Classifier

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. We investigated the usefulness of C-fuzzy decision tree for developing IDS with a data partition based on horizontal fragmentation. Emphirical results indicate the usefulness of our approach in developing the efficient IDS. This paper also used a modified fuzzy C-means algorithm with controllable membership ratio through an extended distance measure to include an additional higher order term. Effect of different membership ratio on the developed decision tree with each fragment is tested to select the best membership ratio. The results obtained have shown that our improved version performs better resulting in an effective intrusion detection system.