INTERNET TRAFFIC CLASSIFICATION USING HYBRID AGGREGATED CLASSIFIER AND NEURAL NETWORK

Abstract

Internet traffic classification is a fundamental technology for modern network security such as quality of service (QoS) control. It is useful to tackle a number of network security problems including lawful interception and intrusion detection. There is an increasing demand on the development of modern traffic classification techniques due to the development of different application. In this work, Internet traffic is carried out by using the supervised classification techniques namely the Neural Network such as Multilayer perceptron (MLP) and Radial base function (RBF) and Hybrid Aggregated Classifier. The task involved in this work is IP packet capturing, Preprocessing, Flow container construction (If the flows observed in a certain period of time share the same destination IP, port, and transport layer protocol, they are determined as correlated flows and modeled as “Flow Container”), separating low density and high density flow, feature extraction and classification. The accuracy of the classifier Hybrid aggregated classification is better than Neural Network