Detection & Classification of Network Anomalies using SVM and Decision Tree

Here in this paper a new technique of detecting network anomalies in the traffic is implemented using the concept of Support vector machine and decision tree. The idea is to first apply clustering of the data traffic using support vector machine and then classifying the network traffic using vertical partition based id3 decision tree algorithm. The proposed technique implemented here provides high accuracy of detecting network anomalies as well as providing less time complexity and less error rate.