International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 8, Issue 2 (February 2021), Pages: 106-116

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 Original Research Paper

 Title: Nominate of significant features for unknown internet traffic applications filtering based on a neural network algorithm

 Author(s): Abdulbasit Abbas Mohamed 1, *, Ahmed Hamza Osman 2, Abdelwahed Motwakel 1, Hani Moetque Aljahdali 2

 Affiliation(s):

 1Faculty of Computer Science and Information Technology, Omdurman Islamic University, Khartoum, Sudan
 2Faculty of Computing and Information Technology in Rabigh, King Abdulaziz University, Rabigh, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-1486-7468

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2021.02.015

 Abstract:

The evolution of the internet into a large, complex service-based network has posed tremendous challenges for network monitoring and control in terms of how to collect massive volumes of data, in addition to the accurate classification of new emerging applications, such as peer-to-peer networks, streaming content and online games. In this work, machine learning algorithms are used for the classification of traffic into their corresponding applications. Furthermore, this research uses our customized training data set collected from the three institutions' campuses. The effect on the size of the training data set has been considered before examining the accuracy of various classification algorithms and selecting the best from a large amount of data traffic in the network, which has led to delays in performance; therefore, to solve this problem we suggested a distinct approach using multiple neural networks with the feature selection in order to predict and identify known and unknown applications. By applying the proposed method, we get excellent accuracy in the classification of data traffic in the network of up to 99.11%, which leads to improved data traffic in the network and avoids delays. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Detection, Classification, Feature selection, Semantic role, Unknown application

 Article History: Received 13 July 2020, Received in revised form 17 October 2020, Accepted 22 October 2020

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

  Mohamed AA, Osman AH, and Motwakel A et al. (2021). Nominate of significant features for unknown internet traffic applications filtering based on a neural network algorithm. International Journal of Advanced and Applied Sciences, 8(2): 106-116

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 

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 References (28)

  1. Arnx A (2018). Understand and create a Perception. In the First Neural Network for Beginners Explained (with code), CS Student, France.   [Google Scholar]
  2. Auld T, Moore AW, and Gull SF (2007). Bayesian neural networks for internet traffic classification. IEEE Transactions on Neural Networks, 18(1): 223-239. https://doi.org/10.1109/TNN.2006.883010   [Google Scholar] PMid:17278474
  3. Bernaille L, Teixeira R, Akodkenou I, Soule A, and Salamatian K (2006). Traffic classification on the fly. ACM SIGCOMM Computer Communication Review, 36(2): 23-26. https://doi.org/10.1145/1129582.1129589   [Google Scholar]
  4. Cao J, Fang Z, Zhang D, and Qu G (2015). Network traffic classification using feature selection and parameter optimization. Journal of Communications, 10(10): 828-835. https://doi.org/10.12720/jcm.10.10.828-835   [Google Scholar]
  5. Erman J, Arlitt M, and Mahanti A (2006). Traffic classification using clustering algorithms. In the 2006 SIGCOMM Workshop on Mining Network Data, Association for Computing Machinery, Pisa, Italy: 281-286. https://doi.org/10.1145/1162678.1162679   [Google Scholar]
  6. Ertam F and Avcı E (2016). Network traffic classification via kernel based extreme learning machine. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue): 109-113. https://doi.org/10.18201/ijisae.267522   [Google Scholar]
  7. Finamore A, Mellia M, and Meo M (2011). Mining unclassified traffic using automatic clustering techniques. In the International Workshop on Traffic Monitoring and Analysis, Springer, Vienna, Austria: 150-163. https://doi.org/10.1007/978-3-642-20305-3_13   [Google Scholar]
  8. Gunnar A, Abrahamsson H, and Söderqvist M (2005). Performance of traffic engineering in operational IP networks–an experimental study. In the International Workshop on IP Operations and Management, Springer, Barcelona, Spain: 202-211. https://doi.org/10.1007/11567486_21   [Google Scholar]
  9. Kim H, Claffy KC, Fomenkov M, Barman D, Faloutsos M, and Lee K (2008). Internet traffic classification demystified: Myths, caveats, and the best practices. In the 2008 ACM CoNEXT Conference, Association for Computing Machinery, Madrid, Spain: 1-12. https://doi.org/10.1145/1544012.1544023   [Google Scholar]
  10. Liu H and Motoda H (2007). Computational methods of feature selection. CRC Press, Boca Raton, USA. https://doi.org/10.1201/9781584888796   [Google Scholar]
  11. Lotfollahi M, Siavoshani MJ, Zade RSH, and Saberian M (2020). Deep packet: A novel approach for encrypted traffic classification using deep learning. Soft Computing, 24(3): 1999-2012. https://doi.org/10.1007/s00500-019-04030-2   [Google Scholar]
  12. Ma J, Levchenko K, Kreibich C, Savage S, and Voelker GM (2006). Unexpected means of protocol inference. In the 6th ACM SIGCOMM Conference on Internet Measurement, Association for Computing Machinery, Rio de Janeiro, Brazil: 313-326. https://doi.org/10.1145/1177080.1177123   [Google Scholar]
  13. Mao KZ (2002). Fast orthogonal forward selection algorithm for feature subset selection. IEEE Transactions on Neural Networks, 13(5): 1218-1224. https://doi.org/10.1109/TNN.2002.1031954   [Google Scholar] PMid:18244519
  14. McGregor A, Hall M, Lorier P, and Brunskill J (2004). Flow clustering using machine learning techniques. In the International Workshop on Passive and Active Network Measurement, Springer, Antibes, France: 205-214. https://doi.org/10.1007/978-3-540-24668-8_21   [Google Scholar]
  15. Moore AW and Zuev D (2005). Internet traffic classification using bayesian analysis techniques. In the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, Banff, Canada: 50-60. https://doi.org/10.1145/1064212.1064220   [Google Scholar]
  16. Namdev N, Agrawal S, and Silkari S (2015). Recent advancement in machine learning based internet traffic classification. Procedia Computer Science, 60: 784-791. https://doi.org/10.1016/j.procs.2015.08.238   [Google Scholar]
  17. Nguyen TT and Armitage G (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys and Tutorials, 10(4): 56-76. https://doi.org/10.1109/SURV.2008.080406   [Google Scholar]
  18. Osman AH and Aljahdali HM (2017). Diabetes disease diagnosis method based on feature extraction using K-SVM. International Journal of Advanced Computer Science and Applications, 8: 236-244. https://doi.org/10.14569/IJACSA.2017.080130   [Google Scholar]
  19. Oudah H, Ghita BV, and Bakhshi T (2019). A novel features set for internet traffic classification using burstiness. In the International Conference on Information Systems Security and Privacy, Prague, Czechia: 397-404. https://doi.org/10.5220/0007384203970404   [Google Scholar]
  20. Sharma A, Pujari AK, and Paliwal KK (2007). Intrusion detection using text processing techniques with a kernel based similarity measure. Computers and Security, 26(7-8): 488-495. https://doi.org/10.1016/j.cose.2007.10.003   [Google Scholar]
  21. Sun G, Li S, Chen T, Su Y, and Lang F (2017). Traffic classification based on incremental learning method. In the International Conference on Advanced Hybrid Information Processing, Springer, Harbin, China: 341-348. https://doi.org/10.1007/978-3-319-73317-3_40   [Google Scholar]
  22. Sun G, Liang L, Chen T, Xiao F, and Lang F (2018). Network traffic classification based on transfer learning. Computers and Electrical Engineering, 69: 920-927. https://doi.org/10.1016/j.compeleceng.2018.03.005   [Google Scholar]
  23. Wang Y, Xiang Y, and Yu SZ (2010). An automatic application signature construction system for unknown traffic. Concurrency and Computation: Practice and Experience, 22(13): 1927-1944. https://doi.org/10.1002/cpe.1603   [Google Scholar]
  24. Wang Y, Xiang Y, Zhang J, Zhou W, Wei G, and Yang LT (2013). Internet traffic classification using constrained clustering. IEEE Transactions on Parallel and Distributed Systems, 25(11): 2932-2943. https://doi.org/10.1109/TPDS.2013.307   [Google Scholar]
  25. Zander S, Nguyen T, and Armitage G (2005). Automated traffic classification and application identification using machine learning. In The IEEE Conference on Local Computer Networks 30th Anniversary, IEEE, Sydney, Australia: 250-257. https://doi.org/10.1109/LCN.2005.35   [Google Scholar] PMid:15351913
  26. Zhang J, Chen C, Xiang Y, and Zhou W (2013a). Robust network traffic identification with unknown applications. In the 8th ACM SIGSAC symposium on Information, computer and communications security, Association for Computing Machinery, Hangzhou, China: 405-414. https://doi.org/10.1145/2484313.2484366   [Google Scholar]
  27. Zhang J, Xiang Y, Zhou W, and Wang Y (2013b). Unsupervised traffic classification using flow statistical properties and IP packet payload. Journal of Computer and System Sciences, 79(5): 573-585. https://doi.org/10.1016/j.jcss.2012.11.004   [Google Scholar]
  28. Zhao JJ, Huang XH, Qiong SUN, and Yan MA (2008). Real-time feature selection in traffic classification. The Journal of China Universities of Posts and Telecommunications, 15: 68-72. https://doi.org/10.1016/S1005-8885(08)60158-2   [Google Scholar]