Network Traffic Classification via Kernel Based Extreme Learning Machine

Abstract

The classification of data on the internet in order to make internet use more efficient has an important place especially for network administrators managing corporate networks. Studies for the classification of internet traffic have increased recently. By these studies, it is aimed to increase the quality of service on the network, use the network efficiently, create the service packages and offer them to the users. The first classification method used for the classification of the internet traffic was the classification for the use of port numbers. This classification method has already lost its validity although it was an effective and quick method of classification for the first usage times of the internet. Another classification method used for the classification of network traffic is called as load-based classification or deep packet analysis. This approach is based on the principle of classification by identifying signatures on packets flowing on the network. Another method of classification of the internet traffic which is commonly used in our day and has been also selected for this study is the kernel based on extreme learning machine based approaches. In this study, over 95% was achieved accuracies using different activation functions.

Keywords

• Machine Learning,Classification,Extreme Learning Machine,Network Classification

References

1. [1] K. Xu, F. Wang and L. Gu (2014). Behavior analysis of internet traffic via bipartite graphs and one-mode projections. Networking, IEEE/ACM Transactions on, 22(3), 931– 942.
2. [2] M. Roughan, S. Sen, O. Spatscheck and N. Duffield (2004). Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification, Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, 135 – 148.
3. [3] X. Zang, A. Tangpong, G. Kesidis and D.J. Miller (2011). Botnet detection through fine flow classification, Departments of CS&E and EE, The Pennsylvania State University, University Park, PA, Report No. CSE11-001.
4. [4] I. Ismail, M.N. Marsono and S.M. Nor (2010). Detecting worms using data mining techniques: learning in the presence of class noise. In Signal-Image Technology and Internet-Based Systems (SITIS), 187-194.
5. [5] M. Soysal and E.G. Schmidt (2010). Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation, 67(6), 451-467.
6. [6] T. Karagiannis, K. Papagiannaki and M. Faloutsos (2005). BLINC: multilevel traffic classification in the dark. In ACM SIGCOMM Computer Communication Review 35(4), pp. 229-240.
7. [7] T.T. Nguyen and G.A. Armitage (2008). Survey of techniques for internet traffic classification using machine learning. Communications Surveys & Tutorials, IEEE, 10(4), pp. 56-76.
8. [8] A. Callado, C. Kamienski, G. Szabo, B. P. Gero, J. Kelner, S. Fernandes and D.Sadok (2009). A Survey on Internet Identification, IEEE Communications Survey and Tutorials, 11(3), pp 37-52.

9. [9] IANA, Internet Assigned Numbers Authority [Online] Available: http://www.iana.org/protocols
10. [10] T. Karagiannis, A. Broido, M. Faloutsos and K. Claffy (2004). Transport Layer Identification of P2P Traffic, IMC’04 Proceeding of the 4th SIGCOMM Conference on Internet measurement, (ACM New York, New York, U.S.A) pp. 121-134
11. [11] F. Dehghani, N. Movahhedinia and M. R. Khayyambashi (2010). Real-time Traffic Classification Based on Statistical and Payload Content Features, IEEE ISA, pp. 1-4.
12. [12] J. Erman, A. Mahanti and M. Arlitt (2006). Internet Traffic Identification Using Machine Learning, IEEE GLOBECOM, pp. 1-6.
13. [13] L. Yingqiu, L. Wei, L. Yunchun (2007). Network Traffic Classificaion Using K-means Clustering, IEEE IMSCCS, pp. 360-365.
14. [14] A. W. Moore and K. Papagiannaki (2005). Toward the Accurate Identification of Network Applications, SpringerLink, PAM Lecture Notes in Computer Science, 3431(1), pp. 41-54.
15. [15] J. Erman, M. Arlitt and A. Mahanti (2006). Traffic Classification Using Clustering Algorithms, MineNet'06 Proceedings of the 2006 SIGCOMM workshop on Mining network data, pp. 281-286.
16. [16] K. Singh and S. Agrawal (2011). Comparative Analysis of Five Machine Learning Algorithms for IP Traffic Classification, International Conference on Emerging Trends in Networks and Computing Communications (ETNCC), pp. 33-38.
17. [17] N. Williams, S. Zender and G. Armitage, Evaluating Machine Learning Algorithms for Automated Network Application Identification, Centre for Advanced Internet Architectures (CAIA), Technical Report 060410B, 2006.
18. [18] N. Williams, S. Zender and G. Armitage (2006). A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification, ACM SIGCOMM Computer Communication Review, 36(5), pp. 5-16.
19. [19] S. Agrawal and K. Singh (2011). Performance Evaluation of Five Machine Learning Algorithms and Three Feature Selection Algorithms for IP Traffic Classification, IJCA Special Issue on Evolution in Networks and Computer Communications, pp. 25-32.
20. [20] S. Agrawal and K. Singh (2011). Feature Extraction based IP Traffic Classification using Machine Learning, Proceeding of the International Conference on Advances in Computing and Artificial Intelligence, pp. 208-212.
21. [21] F. Ertam and E. Avcı (2017). A new approach for internet traffic classification: GA-WK-ELM. Measurement, 95, 135-142.
22. [22] L. Bernaille, R. Teixeira, I. Akodkenou, A. Soule and K. Salamatian (2006). Traffic Classification On The Fly, ACM Special Interest Group on Data Communication Computer Communication Review, 36(2).
23. [23] G.B. Huang, Q.Y. Zhu and C.K. Siew (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1).
24. [24] G.B. Huang and L. Chen (2007). Convex incremental extreme learning machine, Neurocomputing, 70(1), pp.3056–3062.
25. [25] G.B. Huang and L. Chen (2008). Enhanced random search based incremental extreme learning machine, Neurocomputing, 71(1), pp. 3460–3468.
26. [26] G.B. Huang, Q.Y. Zhu and C.K. Siew (2004). Extreme learning machine: a new learning scheme of feedforward neural networks. In Neural Networks, Proceedings. IEEE International Joint Conference on 2(1), 985-990.
27. [27] E. Avci and R. Coteli (2012) A new automatic target recognition system based on wavelet extreme learning machine, Expert Systems with Applications, 39(16), 12340-12348.
28. [28] G.B. Huang, H. Zhou, X. Ding and R. Zhang (2012). Extreme learning machine for regression and multiclass classification. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 42(2), 513-529.
29. [29] G.B Huang, X. Ding and H. Zhou (2010). Optimization method based extreme learning machine for classification. Neurocomputing, 74(1), 155-163.
30. [30] G.B. Huang, Q.Y. Zhu and C.K. Siew (2004). Extreme learning machine: a new learning scheme of feedforward neural networks. In Neural Networks, Proceedings. IEEE International Joint Conference on, 2, pp. 985-990.
31. [31] J. Luo, C.M. Vong and P.K. Wong (2014). Sparse Bayesian Extreme Learning Machine for Multi-classification, Neural Networks and Learning Systems, 4(25), 836-843.
32. [32] K.S. Banerjee (1973). Generalized inverse of matrices and its applications, Technometrics, 1(15), 197-197
33. [33] B. Li, X. Rong and Y. Li (2014). An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures. The Scientific World Journal.
34. [34] A. Moore, D. Zuev and M. Crogan (2005). Discriminators for use in flow-based classification (Queen Mary and Westfield College, Department of Computer Science)
35. [35] F. Ertam and E. Avci (2016). Classification with Intelligent Systems for Internet Traffic in Enterprise Networks. Int'l Journal of Computing, Communications & Instrumentation Engg. (IJCCIE) Vol. 3, Issue 1 (2016) ISSN 2349-1469 EISSN 2349-1477