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Near Real Time Online Flow-Based Internet Traffic Classification Using Machine Learning (C4.5)
abuagla babiker mohammed, Sulaiman Mohd Nor
Pages - 370 - 379     |    Revised - 30-09-2009     |    Published - 21-10-2009
Volume - 3   Issue - 4    |    Publication Date - August 2009  Table of Contents
online , classification, NetFlow, Bandwidth
Offering reliable novel service in modern heterogeneous networks is a key challenge and an important prospective income source for many network operators and providers. Providing reliable future service in a cost effective scalable manner requires efficient use of networking and computing resources. This can be done by making the network more self enabled, i.e. making it capable of making distributed local decisions regarding the utilization of the available resources. However such decisions must be correlated in order to achieve the global overall goal (maximizing the performance and minimizing the cost) Since network administrators are always worried about making fast decisions to monitor and regulate the Internet traffic, a novel approach for online flow-based network traffic classification is proposed. This proposal is based on Machine learning algorithm C4.5 and a custom built network traffic data set captured from a university campus environment. Furthermore the aim of this effort is to build a complete online flow based traffic classification and control system. Validation on the proposed system is done from accuracy and time points of views. Firstly, an offline training and testing data sets are applied to Weka’s C4.5 and our system. And their corresponding accuracy has been compared. Our experimental results show that the accuracy is the exactly the same. Secondly, the received UDP NetFlow packets have been send to our system and to a basic packet sniffing program and the number of NetFlow packets has been counted in each. The comparison result show that no packet overwriting due to race condition.
CITED BY (2)  
1 Bo Chun, XIA Jing-bo, Wu Jixiang, Ren Gaoming, & ZHAO Xiao Huan. (2013). Summary of network traffic in real-time classification of Computer Science, 40 (9), 8-15.
2 Adibi, S. (2010, June). An application layer non-repudiation wireless system: A cross-layer approach. In World of Wireless Mobile and Multimedia Networks (WoWMoM), 2010 IEEE International Symposium on a (pp. 1-2). IEEE.
1 Google Scholar 
2 Academic Journals Database 
3 ScientificCommons 
4 CiteSeerX 
5 iSEEK 
6 Socol@r  
7 ResearchGATE 
8 Libsearch 
9 Bielefeld Academic Search Engine (BASE) 
10 Scribd 
11 WorldCat 
12 SlideShare 
13 PdfSR 
15 Chinese Directory Of Open Access 
http://www.iana.org/assignments /port-numbers (last accessed July 2009)
Jeffrey Erman, Anirban Mahanti, Martin Arlitt, Carey Williamson,Identifying and Discriminating Between Web and Peer to Peer Traffic in the Network Core " August 27–31, 2007, ACM
Liu Bin, “Traffic Measurements of BitTorrent System Based on Netfilter “, C2006 IEEE
Nigel Williams, Sebastian Zander, Grenville Armitrage A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification
A.W.Moore and D.papagiannaki, “Toward the accurate Identification of network applications”, in poc. 6th passive active measurement. Workshop (PAM), mar 2005,vol. 3431, pp 41-54
Abuagla Babiker, Suliaman Mohd Nor. “Performance Evaluation of Decision Tree Algorithms for Flow-Based Network Traffic Classification IGCES2008, International Graduate Conference of Science and Engineering, UTM Johore.
Abuagla Babiker, Suliaman Mohd Nor. “Towards a Flow-based Internet Traffic Classification For Bandwidth Optimization” International journal of Computer Science and Security” may 2009
Alok Madhukar Carey Williamson, “A Longitudinal Study of P2P Traffic Classification”, Proceedings of the 2th IEEE International Symposium on (MASCOTS '06) 2006 IEEE
Bernaille, L., Teixeira, R., Akodkenou, I., Soule, A., and Salamatian, K. 2006. Traffic classification on the fly. SIGCOMM Comput. Commun. Rev. 36, 2 (Apr. 2006), 23-26. DOI= http://doi.acm.org/10.1145/1129582.1129589
C. Dews, A. Wichmann, and A. Feldmann."An analysis of Internet chat systems". In IMC’03, Miami Beach, USA, Oct 27-29, 2003.
Guangxing ZHANG, Gaogang XIE, Jianhua YANG, Yinghua MIN, Zhaomin ZHOU,Xiaodong DUAN, “Accurate Online Traffic Classification with Multi-phases Identification Methodology”, Consumer Communications and Networking Conference, 2008. CCNC 2008. 5th IEEE, Page(s):141 – 146, 10-12 Jan. 2008
Hongbo Jiang, Andrew W.Moore, et al “Lightweight Application Classification for Network Management” ACM 2007
http://www.cs.waikato.ac.nz/ml/weka/ (last access nov 2008)
J. Ross Quainlan, “C 4.5: Programs for Machine Learning “ Morgan Kaufman Publisher, 1993
Jeffrey Erman, Martin Arlitt, Anirban Mahanti, "Traffic Classification Using Clustering Algorithms", in SIGCOMM’06 Workshops September 11-15, 2006, Pisa, Italy.
Karagiannis, T., Papagiannaki, K., and Faloutsos, M. 2005. BLINC: multilevel traffic classification in the dark. In Proceedings of the 2005 Conference on Applications, Technologies, Architectures, and Protocols For Computer Communications (Philadelphia, Pennsylvania, USA, August 22 - 26, 2005). SIGCOMM '05. ACM, New York, NY, 229-240. DOI= http://doi.acm.org/10.1145/1080091.1080119
Li Jun; Zhang Shunyi; Lu Yanqing; Zhang Zailong, "Internet Traffic Classification Using Machine Learning," Communications and Networking in China, 2007. CHINACOM '07. Second International Conference on , vol., no., pp.239-243, 22-24 Aug. 2007.
M.S. Kim, H.J. Kang, J.W. Hong, 2003, Towards peer-to-peer traffic analysis using flows, Working paper obtained from the Distributed Processing and Network Management Laboratory. Department of Computer Science and Engineering, Pohang University of Science and Technology, Republic of Korea.
M.S. Kim, H.J. Kang, J.W. Hong, 2003, Towards peer-to-peer traffic analysis using flows, Working paper obtained from the Distributed Processing and Network Management Laboratory. Department of Computer Science and Engineering, Pohang University of Science and Technology, Republic of Korea.
Nguyen, T.T.T.; Armitage, G., "A survey of techniques for Internet traffic classification using machine learning," Communications Surveys & Tutorials, IEEE, vol.10, no.4, pp.56-76, Fourth Quarter 2008
P. Haffner, S. Sen, O. Spatscheck, and D. Wang. ACAS: "Automated Construction of Application Signatures". In SIGCOMM’05 MineNet Workshop, Philadelphia, USA, August 22-26, 2005.
Robin Sommer and Anja Feldman, Saarland University, Germany NetFlow: Information loss or win? ACM Measurement Workshop, 2002
S. Sen, O. Spatscheck, and D. Wang. "Accurate, Scalable In-Network Identification of P2P Traffic Using Application Signatures". In WWW2005, New York, USA, May 17-22, 2004.
S. Sen, O. Spatscheck, and D. Wang."Accurate, Scalable In-Network Identi¯cation of P2P Traffic Using Application Signatures. In WWW 2004, New York, USA, May 2004.
T. Karagiannis, A. Broido, and N. Brownlee. Is P2P Dying or Just Hiding? In GLOBECOM '04, Dallas, USA, November 2004.
T. Karagiannis, A. Broido, M. Faloutsos, and K. cla®y. "Transport Layer Identi¯cation of P2P Traffic. In IMC'04, Taormina, Italy, October 2004.
Mr. abuagla babiker mohammed
- Malaysia
Dr. Sulaiman Mohd Nor
utm - Malaysia