DocumentCode :
3575518
Title :
Early detection of VoIP network flows based on sub-flow statistical characteristics of flows using machine learning techniques
Author :
Sinam, Tejmani ; Ngasham, Nandarani ; Lamabam, Pradeep ; Singh, Irengbam Tilokchan ; Nandi, Sukumar
Author_Institution :
Dept. of Comput. Sci., Manipur Univ., Imphal, India
fYear :
2014
Firstpage :
1
Lastpage :
6
Abstract :
Network traffic classification plays an important role in the areas of network security, network monitoring, QoS and traffic engineering. In this paper, we design a network traffic classifier based on the statistical features extracted from network flows. Instead of deriving the statistical characteristics per flow, our model make use of features extracted from the first few seconds of each flows. The first few seconds of each flow is divided into overlapping time-based windows. This approach enables our classifier to classify each flow early. Attribute selection algorithms Chi-Square, CON and CFS are used to obtain the optimal subset of features. We give a comparative analysis of the result on the said approach based on the classification algorithms (Decision tree (C4.5), Naive Bayes, Bayesian Belief Network and SVM). We also present a single class classifier implementation of C4.5 algorithm. The experimental results show that the proposed method can achieve over 99% accuracy for all testing dataset. Using the proposed method, C4.5 algorithm delivers high speed and accuracy. By taking inference from these offline classifiers, we build an online standalone classifier using C/C++. We used the following applications: Skype, Gtalk and Asterisk.
Keywords :
Internet telephony; belief networks; learning (artificial intelligence); quality of service; telecommunication security; telecommunication traffic; Bayesian belief network; QoS; VoIP network flows; attribute selection algorithms; machine learning techniques; network monitoring; network security; network traffic classification; overlapping time-based windows; sub-flow statistical characteristics; traffic engineering; Accuracy; Algorithm design and analysis; Feature extraction; Media; Ports (Computers); Testing; Training; Machine Learning; Statistical Network Traffic Classification; Traffic Behavioural Classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Networks and Telecommuncations Systems (ANTS), 2014 IEEE International Conference on
Print_ISBN :
978-1-4799-5867-2
Type :
conf
DOI :
10.1109/ANTS.2014.7057227
Filename :
7057227
Link To Document :
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