Implementation of an Effective Internet Network Traffic Classification using Semi-Supervised Method

Internet traffic classification is an important tool for network management. It allows operators to better predict future traffic matrices and demands, security personnel to detect anomalous behavior, and researchers to develop more realistic traffic models. We present here a traffic classifier that can achieve a high accuracy across a range of application types without any source or destination host-address or port information. We use a Semi-Supervised method for classification of network traffic. The proposed method classifies network traffic using flow statistics that allows classifiers to be designed from training data consisting of only a few labeled and many unlabeled flows. The approach consists of two steps, clustering and classification. Clustering partitions the training data set into clusters. After making clusters, classification is performed in which labeled data are used for assigning class labels to the clusters. The results showed that the proposed method outperforms to existing method. During more specific comparison we use KDD (Knowledge Discovery in Databases) data set and tested our proposed and existing algorithm, we show the proposed algorithm is better than existing algorithm. The results are represented in Receiver operating characteristics (ROC) graphs.ROC graphs are a very useful tool for visualizing and evaluating classifiers.