Study of Tree Base Data Mining Algorithms for Network Intrusion Detection

Internet growth has increased rapidly due to which number of network attacks have been increased. This emphasis importance of network intrusion detection systems (IDS) for securing the network. It is the process of monitoring and analyzing network traffic for detecting security violations many researcher suggested data mining technique such as classification, clustering ,pattern matching and rule induction for developing an effective intrusion detection system. In order to detect the intrusion, the network traffic can be classified into normal and anomalous. In this paper we have evaluated tree base classification algorithms namely J48, Hoeffding tree, Random Forest, Random Tree, REPTree. The comparison of these tree based classification algorithms is presented in this paper based upon their performance metrics using 10 fold cross validation and KDD- CUP test dataset. This study shows that random forest and J48 are the best suitable tree base algorithms