Coarse-Grained Classification Of P2p Network Traffic Using Filter/Wrapper Features Selection

International Journal of P2P Network Trends and Technology (IJPTT)          
© 2019 by IJPTT Journal
Volume-9 Issue-5
Year of Publication : 2019
Authors : Haitam A. Jamil, Bushra M. Ali, Ahmed E. Osman, Hind G. Abdelrahim
DOI :  10.14445/22492615/IJPTT-V9I5P402


MLA Style: Haitam A. Jamil, Bushra M. Ali, Ahmed E. Osman, Hind G. Abdelrahim "Coarse-Grained Classification Of P2p Network Traffic Using Filter/Wrapper Features Selection" International Journal of P2P Network Trends and Technology 9.5 (2019): 13-16.

APA Style:Haitam A. Jamil, Bushra M. Ali, Ahmed E. Osman, Hind G. Abdelrahim(2019). Coarse-Grained Classification Of P2p Network Traffic Using Filter/Wrapper Features Selection. International Journal of P2P Network Trends and Technology, 9(5), 13-16.


Classifying network traffic applications is needed for network security and controlling. The emergence of new Internet applications with the use of encryption techniques, gains significant attention in the last period of time. However, the problem of using huge features requires longer processing time as well as low classification accuracy. Therefore, feature selections have a significant impact on classification performance. In this paper, we propose Filter/Wrapper feature selection methods for flow-based Internet traffic Classification using Machine Learning techniques. The evaluation has been carried out through experiments on the traffic traces downloaded from different shared resources. The experiments demonstrate our approach can greatly improve the computational performance.


[1] Jamil, H.A., Bushra M. A , Ahmed Abdalla , Ban M. K , Sulaiman M. Nor , Muhammad N. , Improving P2P Network Traffic Classification with ML multi-classifiers. International Journal of P2P Network Trends and Technology (IJPTT), 2014. Volume - 4(Issue - 2 ).
[2] AH, H. and H.A. Jamil, Enhance the accuracy of Machine Learning Internet Traffic Classifier by Applying Datasets Validation Issues and Using a Hybrid Classifier. 2013.
[3] Ibrahim, H.A.H., S.M. Nor, and H.A. Jamil. Online hybrid internet traffic classification algorithm based on signature statistical and port methods to identify internet applications. in Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on. 2013. IEEE.
[4] Jamil, H.A., et al., Selection of online Features for Peer-to-Peer Network Traffic Classification, in Recent Advances in Intelligent Informatics. 2014, Springer International Publishing. p. 379-390.
[5] MA, B., et al. Multi-stage Feature Selection for On-Line Flow Peer-to-Peer Traffic Identification. in Asian Simulation Conference. 2017. Springer.
[6] Kira, K. and L.A. Rendell. The feature selection problem: Traditional methods and a new algorithm. in AAAI. 1992.
[7] Narendra, P.M. and K. Fukunaga, A branch and bound algorithm for feature subset selection. Computers, IEEE Transactions on, 1977. 100(9): p. 917-922.
[8] Koller, D. and M. Sahami, Toward optimal feature selection. 1996.
[9] Erman, J., et al. Semi-supervised network traffic classification. in ACM SIGMETRICS Performance Evaluation Review. 2007. ACM.
[10] Dash, M. and H. Liu, Feature selection for classification. Intelligent data analysis, 1997. 1(1-4): p. 131-156.
[11] Molina, L.C., L. Belanche, and À. Nebot. Feature selection algorithms: A survey and experimental evaluation. in Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on. 2002. IEEE.
[12] Bins, J. and B.A. Draper. Feature selection from huge feature sets. in Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on. 2001. IEEE.
[13] Bermejo, P., et al., Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowledge-Based Systems, 2012. 25(1): p. 35-44.
[14] Foithong, S., O. Pinngern, and B. Attachoo, Feature subset selection wrapper based on mutual information and rough sets. Expert Systems with Applications, 2012. 39(1): p. 574-584.
[15] Cadenas, J.M., M.C. Garrido, and R. Martínez, Feature subset selection Filter–Wrapper based on low quality data. Expert Systems with Applications, 2013. 40(16): p. 6241-6252.
[16] Moore, A.W. and D. Zuev. Internet traffic classification using bayesian analysis techniques. 2005. ACM.
[17] Bernaille, L., et al., Traffic classification on the fly. ACM SIGCOMM Computer Communication Review, 2006. 36(2): p. 23-26.
[18] Jun, L., et al. P2P traffic identification technique. in Computational Intelligence and Security, 2007 International Conference on. 2007. IEEE.
[19] Yang, Y., et al. Solving P2P traffic identification problems Via optimized support vector machines. in Computer Systems and Applications, 2007. AICCSA`07. IEEE/ACS International Conference on. 2007. IEEE.
[20] Auld, T., A.W. Moore, and S.F. Gull, Bayesian neural networks for internet traffic classification. Neural Networks, IEEE Transactions on, 2007. 18(1): p. 223-239.
[21] Moore, A.W., D. Zuev, and M. Crogan, Discriminators for use in flow-based classification. 2005, Technical report, Intel Research, Cambridge.

Coarse-grained classification; features selection; wrapper approach; filter method.