Literature Review of Network Traffic Classification Using Neural Networks

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 157)

Abstract

The management and surveillance of the network operation are vital to the managers of the network. However the traditional network management software or tools can not archive this objective. Currently there are mainly three methods for network traffic classification and recognition, one is rely on ‘well known’ TCP or UDP port numbers, second is deeply packet inspection and the third is based on features of traffic flow. The first two methods both have some shortcomings. while the third method can be through selecting the different pattern recognition methods, Such as linear models for classification, kernel methods, clustering methods and neural networks methods to overcome this shortcoming by identifying network applications based on per-flow statistics, derived from payload-independent features such as packet length and inter-arrival time distributions and so on. In this literature review, with the articles we get from the Google scholar, analyze the features these article use to classify the traffic by using the neural networks, and the classification accuracy of these articles announced. Finally discuss the improvement of the algorithms these articles adopted.

Keywords

Methodological Criterion Network Intrusion Detection Traffic Classification Fuzzy ARTMAP Bayesian Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Centre of Network Information Inner Mongolia UniversityHohhotChina
  2. 2.College of Computer Science Inner Mongolia UniversityHohhotChina

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