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Cor-ENTC:correlation with ensembled approach for network traffic classification using SDN technology for future networks

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Abstract

According to a study on advanced technology, resource planning, and design for Fifth Generation (5G) architecture and elsewhere, network traffic classification is a basic and complex part of software-defined networking (SDN). 5G requires end-to-end security that uses its software-defined architecture to automatically monitor the network and classify the traffic flows. To improve the security in network traffic classification, it is necessary to apply a correct set of policy rules to classify future network traffic flows. To create a communication-efficient and intelligent traffic classification framework in the SDN environment, machine learning is used between the data and the control planes. The proposed ensemble learning pre-processing tool to categorize incoming VPN traffic by applying a possible set of policies. The proposed technique is compared with existing classifiers and has a higher accuracy of 98% to 99.9% for ensemble models than single classifiers and other existing options, according to an evaluation of performance.

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References

  1. Foukas X, Patounas G, Elmokashfi A et al (2017) Network slicing in 5g: Survey and challenges. IEEE Commun Mag 55(5):94–100. https://doi.org/10.1109/MCOM.2017.1600951

    Article  Google Scholar 

  2. Popovski P, Trillingsgaard KF, Simeone O et al (2018) 5g wireless network slicing for embb, urllc, and mmtc: a communication-theoretic view. IEEE Access 6:55,765-55,779

    Article  Google Scholar 

  3. Kobbaey T, Hamzaoui R, Ahmad S et al (2021) Enhanced collision resolution and throughput analysis for the 802.11 distributed coordination function. Inter J Commun Syst 34(16):e4953

    Article  Google Scholar 

  4. Afolabi I, Taleb T, Samdanis K et al (2018) Network slicing and softwarization: a survey on principles, enabling technologies, and solutions. IEEE Commun Survey Tutor 20(3):2429–2453

    Article  Google Scholar 

  5. on 5G Architecture:5G PPP Architecture Working Group V (2014) 5g architecture. https://5g-ppp.eu/wp-content/uploads/2014/02/5G-PPP-5G-Architecture-WP-For-public-consultation.pdf/

  6. Wang M, Cui Y, Wang X et al (2017) Machine learning for networking: Workflow, advances and opportunities. IEEE Network 32(2):92–99. https://doi.org/10.1109/MNET.2017.1700200

    Article  Google Scholar 

  7. Nunes BAA, Mendonca M, Nguyen XN et al (2014) A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Commun surveys Tutor 16(3):1617–1634. https://doi.org/10.1109/SURV.2014.012214.00180

    Article  Google Scholar 

  8. Abbas K, Khan TA, Afaq M et al (2021) Network slice lifecycle management for 5g mobile networks: An intent-based networking approach. IEEE Access 9:80,128-80,146

    Article  Google Scholar 

  9. Xie J, Yu FR, Huang T et al (2018) A survey of machine learning techniques applied to software defined networking (sdn): Research issues and challenges. IEEE Commun Surveys Tutor 21(1):393–430. https://doi.org/10.1109/comst.2018.2866942

    Article  Google Scholar 

  10. Adedeji PA (2020) Hybrid Renewable Energy-Based Facility Location: A Geographical Information System (GIS) Integrated Multi-Criteria Decision-Making (MCDM) Approach. University of Johannesburg (South Africa)

  11. Cisco (2021) Global traffic. https://www.cisco.com/

  12. Dataset T (2016) Dataset details. https://www.unb.ca/cic/datasets/vpn.html

  13. Rezaei S, Liu X (2018) How to achieve high classification accuracy with just a few labels: A semi-supervised approach using sampled packets. arXiv preprint https://doi.org/10.48550/arXiv.1812.09761arXiv:1812.09761

  14. Żelasko D, Pławiak P (2021) Ensemble learning techniques for transmission quality classification in a pay &require multi-layer network. Inter J Appl Math Comput Sci 31(1). https://doi.org/10.34768/amcs-2021-0010

  15. Parsaei MR, Sobouti MJ, Khayami SR et al (2017) Network traffic classification using machine learning techniques over software defined networks. Inter J Adv Comput Sci Appl 8(7):220–225

    Google Scholar 

  16. Perera Jayasuriya Kuranage M, Piamrat K, Hamma S (2019) Network traffic classification using machine learning for software defined networks. In: International Conference on Machine Learning for Networking, Springer, pp 28–39, https://doi.org/10.14569/IJACSA.2017.080729

  17. Audah M, Chin TS, Zulfadzli Y, et al (2019) Towards efficient and scalable machine learning-based qos traffic classification in software-defined network. In: International Conference on Mobile Web and Intelligent Information Systems, Springer, pp 217–229, https://doi.org/10.1007/978-3-030-27192-3_17

  18. Afuwape AA, Xu Y, Anajemba JH et al (2021) Performance evaluation of secured network traffic classification using a machine learning approach. Comput Standards Interf 78(103):545. https://doi.org/10.1016/j.csi.2021.103545

    Article  Google Scholar 

  19. Amaral P, Dinis J, Pinto P, et al (2016) Machine learning in software defined networks: Data collection and traffic classification. In: 2016 IEEE 24th International Conference on Network Protocols (ICNP), IEEE, pp 1–5, https://doi.org/10.1109/ICNP.2016.7785327

  20. Yang T, Vural S, Qian P, et al (2021) Achieving robust performance for traffic classification using ensemble learning in sdn networks. In: ICC 2021-IEEE International Conference on Communications, IEEE, pp 1–6, https://doi.org/10.1109/ICC42927.2021.9500571

  21. Nakao A, Du P (2018) Toward in-network deep machine learning for identifying mobile applications and enabling application specific network slicing. IEICE Transactions on Communications p 2017CQI0002

  22. Raikar MM, Meena S, Mulla MM et al (2020) Data traffic classification in software defined networks (sdn) using supervised-learning. Procedia Comput Sci 171:2750–2759. https://doi.org/10.1016/j.procs.2020.04.299

    Article  Google Scholar 

  23. Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learning

  24. JohannesFischer (2021) Correlation based feature selection. https://johfischer.com/2021/08/06/correlation-based-feature-selection-in-python-from-scratch/

  25. Nyaramneni S, Saifulla M, Mehra SS (2019) Internet traffic prediction in sdn using rf and xgb. In: International Conference on Computational and Bio engineering, Springer, pp 153–159, https://doi.org/10.1007/978-3-030-46943-6_18

  26. Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining, pp 785–794, https://doi.org/10.48550/arXiv.1603.02754

  27. Li W, Moore AW (2007) A machine learning approach for efficient traffic classification. In: 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, IEEE, pp 310–317, https://doi.org/10.1109/MASCOTS.2007.2

  28. Wang X, Deng Q, Ren J et al (2019) The joint optimization of online traffic matrix measurement and traffic engineering for software-defined networks. IEEE/ACM Transact Netw 28(1):234–247. https://doi.org/10.1109/TNET.2019.2957008

    Article  Google Scholar 

  29. OpenFlownetwork (2014) Ryuapplications. https://ryu-sdn.org

  30. Kaur K, Singh J, Ghumman NS (2014) Mininet as software defined networking testing platform. In: International Conference on Communication, Computing & Systems (ICCCS), pp 139–42

  31. OpenFlow Switch Specification VWPx (June 25, 2012) https://www.opennetworking.org/wp-content/uploads/2014/10/openflow-spec-v1.3.0.pdf

  32. Kim H, Claffy KC, Fomenkov M, et al (2008) Internet traffic classification demystified: myths, caveats, and the best practices. In: Proceedings of the 2008 ACM CoNEXT conference, pp 1–12, https://doi.org/10.1145/1544012.1544023

  33. Li W, Canini M, Moore AW et al (2009) Efficient application identification and the temporal and spatial stability of classification schema. Comput Netw 53(6):790–809. https://doi.org/10.1016/j.comnet.2008.11.016

    Article  MATH  Google Scholar 

  34. Zhou K, Wang W, Wu C et al (2020) Practical evaluation of encrypted traffic classification based on a combined method of entropy estimation and neural networks. ETRI J 42(3):311–323. https://doi.org/10.4218/etrij.2019-0190

    Article  Google Scholar 

  35. Rasteh A, Delpech F, Aguilar-Melchor C, et al (2021) Encrypted internet traffic classification using a supervised spiking neural network. arXiv preprint https://doi.org/10.48550/arXiv.2101.09818arXiv:2101.09818

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, TamilNadu, 620015, India

    Suguna Paramasivam & R. Leela Velusamy

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  1. Suguna Paramasivam
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  2. R. Leela Velusamy
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Each author who made a contribution to the study’s conceptualization and design contributed to the research activity.

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Correspondence to Suguna Paramasivam.

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Paramasivam, S., Velusamy, R.L. Cor-ENTC:correlation with ensembled approach for network traffic classification using SDN technology for future networks. J Supercomput 79, 8513–8537 (2023). https://doi.org/10.1007/s11227-022-04969-4

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