Department of Information Technology

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    INTERNET TRAFFIC CLASSIFICATION USING HYBRID AGGREGATED CLASSIFIER AND NEURAL NETWORK
    (International Journal Of Engineering And Computer Science(IJECS), 2014-10-10) G, Rubadevi; R, Amsaveni
    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
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    A NOVEL HYBRID AGGREGATED CLASSIFIER FOR INTERNET TRAFFIC CLASSIFICATION
    (International Journal of Computer Engineering and Applications(IJCEA), 2014-08) G, Rubadevi; R, Amsaveni
    The classification and identification of network application from network traffic flow provides various advantages to a number of fields such as security monitoring, intrusion detection and to tackle a number of network security problems including lawful interception. In this paper traffic flow is described by using the discretized statistical features. The flow correlation information of the network traffic flow is modeled by Flow Container (FC). In this paper novel hybrid aggregated classifier is proposed. First, low density flow and high density flow is analyzed. For Low density flow C4.5 classifier is used and high density flow Naïve Bayesian classifier is used and finally aggregated result is provided. The aggregated result is compared with machine learning algorithm such as Single Naïve Bayesian predictor. The proposed system enhances the accuracy rate as well as improves the performance of the system.