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Impact of packet sampling on classification of network traffic by machine learning methods

Keywords:

O.I. Sheluhin – Dr.Sc. (Eng.), Professor, Head of Department “Information Security and Automation”, Moscow Technical University of Communications and Informatics. E-mail: sheluhin@mail.ru Yu.A. Kalugin – Post-graduate Student, Department of “Information Security and Automation”, Moscow Technical University of Communications and Informatics. E-mail: derron210@gmail.com


Classification of network traffic can be used in different fields, such as security control, application prioritization, and intrusion detection. Information passing through operational networks is large, so classification requires expensive equipment and spaces for storing large amounts of data. This problem can be solved by using sampling – selection ofrandom packets from the traffic. That approach can reduce requirements of performance and storage of data for analyze. Limited resources and high capacity of current networks preventdeployment of the classification solutions. For solving classification tasks in this work is used machine learning. There are several problemsin deployment of clas-sification solution: A majority of machine learning algorithms works only with packet datasets, which requires using additional (often, expen-sive) equipment. Impact of packet sampling on the traffic classification still unknown, although networks operators often use it. The purpose of the work is to solve outlined problems by using machine learning methods. To find out the impact of packet sampling on algorithms performance, in this work is used the most common classification algorithms: C4.5, SVM, Ada Boost, Naïve Bayes, Bayes Net. Weka API was used for implementation of the algorithms. For the task traffic was captured for training the algorithms and classification with different sample rates. The following applications were selected for classification: web, p2p, ftp, mail. The experiment shows, that in general, sampling caused deterioration of performance of all algorithms. For applications, which use flows with high duration and size, the impact of sampling is the lowest. Metrics from the field of informational retrieval, such as precision and recall is used for evaluation of performance. In the work is shown dependency of recall and precision from sample rate. Was shown, that sampling causes increasing of type I and type II errors.
References:

 

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