Skip to main content
SpringerLink
Log in

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

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

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

    Suguna Paramasivam & R. Leela Velusamy

Authors
  1. Suguna Paramasivam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Leela Velusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Each author who made a contribution to the study’s conceptualization and design contributed to the research activity.

Corresponding author

Correspondence to Suguna Paramasivam.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical Approval

The proposed work does not include any ethically approved human or animal experiments.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04969-4

Keywords

Search

Navigation