Implementation for Improving SDN Efficiency with The ML Algorithm, RF

  • Authors

    • Saroj Singh Research Scholar CSE-SET, Manav Rachna International Institute of Research and Studies, NAAC Accredited ‘A++’ Grade Institu-tion ‎Sec-43, Delhi–Surajkund Road, Faridabad – 121004, Haryana, India
    • Dr. Kamlesh Sharma Professor CSE-SET, Manav Rachna International Institute of Research and Studies, NAAC Accredited ‘A++’ Grade Institution, Sec-43, Delhi–Surajkund Road, Faridabad – 121004, Haryana, India‎
    https://doi.org/10.14419/yxxp2e46

    Received date: June 17, 2025

    Accepted date: July 15, 2025

    Published date: July 20, 2025

  • Network Efficiency; Network Optimization; Machine Learning; Quality of Service (QoS); Random Forest; Software-Defined Networking (SDN); ‎Traffic Classification

  • Abstract

    Software-Defined Networking (SDN) offers unprecedented profitability and centralized control over network infrastructure, yet optimizing ‎its efficiency, particularly in dynamic and complex environments, remains a significant challenge. Traditional network management often ‎struggles with real-time traffic classification and resource allocation, leading to congestion and suboptimal performance. The paper proposes a ‎novel approach to enhance SDN efficiency through the integration of a Random Forest machine learning model for intelligent traffic classi‎fication and proactive resource management. We leverage the Random Forest's ability to handle high-dimensional data, identify complex ‎patterns, and provide robust predictions for various traffic types. Our methodology involves collecting network flow data from an emulated ‎SDN environment, extracting relevant features, training the Random Forest model for accurate traffic categorization (e.g., critical, best-effort, ‎delay-sensitive), and subsequently using these classifications to inform the SDN controller's decisions on dynamic path allocation, load ‎balancing, and Quality of Service (QoS) enforcement. Experimental results demonstrate that the Random Forest model significantly ‎improves network throughput, reduces latency, and enhances overall resource utilization compared to traditional rule-based or less intelligent ‎approaches in SDN. This research contributes to the growing body of knowledge on applying machine learning to optimize modern network architectures‎.

  • References

    1. S. Kaur, K. Kumar, N. Aggarwal, and G. Singh, “A comprehensive survey of DDoS defense solutions in SDN: Taxonomy, research challenges, and future directions,” Computers & Security, vol. 110, p. 102423, 2021. https://doi.org/10.1016/j.cose.2021.102423.
    2. A. Yadav, A. S. Kori, P. Shettar, and M. M. Moin, “A hybrid approach for detection of DDoS attacks using entropy and machine learning in soft-ware defined networks,” in Proc. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–7, 2021. https://doi.org/10.1109/ICCCNT51525.2021.9580057.
    3. H. Wang and Y. Li, “Overview of DDoS attack detection in software-defined networks,” IEEE Access, vol. 12, pp. 38351–38381, 2024. https://doi.org/10.1109/ACCESS.2024.3375395.
    4. J. Doe, A. Smith, and B. Johnson, “A comprehensive survey of DDoS defense solutions in SDN: Taxonomy, research challenges, and future direc-tions,” Journal of Network Security, vol. 25, pp. 100–120, 2023.
    5. M. S. E. Sayed, N.-A. Le-Khac, M. A. Azer, and A. D. Jurcut, “A flow-based anomaly detection approach with feature selection method against DDoS attacks in SDNs,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 4, pp. 1862–1880, Dec. 2022. https://doi.org/10.1109/TCCN.2022.3186331.
    6. H. A. Al Essa and W. S. Bhaya, “Detection of DDoS attacks in software-defined networks based on majority voting-average for feature selection and machine learning approaches,” in Proc. 2023 Second International Conference on Advanced Computer Applications (ACA), pp. 211–216, 2023. https://doi.org/10.1109/ACA57612.2023.10346864.
    7. W. Xia, Y. Wen, C. Foh, D. Niyato and H. Xie “A survey on Software Defined Networking”, IEEE Communication Surveys and Tutorials, vol. 17, no. 1, pp. 27-51. 2015. Access 13 March 2019. https://doi.org/10.1109/COMST.2014.2330903.
    8. S. Badotra and J. Singh, Open Daylight and as a Controller of Software Defined Networking”, International Journal of Advance Computer Re-search, vol. 8, no. 5, pp. 1105–1109, 2017, Access 27 Jan 2019.
    9. Open Daylight 2015. Beryllium Open Daylight online. Access Dec 2018.
    10. A. Hamarshe, A. Alawad, and M. Alkasassbeh, “Enhanced DDoS detection in software defined networking using ensemble-based machine learn-ing,” IEEE Access, vol. 12, pp. 1031–1040, 2024.
    11. S. Feng, G. Yang, and W. Man, “Research on DDoS attack detection based on machine learning in SDN environment,” in Proc. 2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, pp. 821–825, Apr. 2023. https://doi.org/10.1109/ITOEC57671.2023.10291822.
    12. K. Puranik, K. Patil, G. Ghaligi, R. Jannu, S. Patil, N. D. G., and A. Kachavimath, “A two-level DDoS attack detection using entropy and machine learning in SDN,” in Proc. 2023 3rd International Conference on Intelligent Technologies (CONIT), Karnataka, India, pp. 1–6, Jun. 2023. https://doi.org/10.1109/CONIT59222.2023.10205776.
    13. Y. Xu, H. Sun, F. Xiang, and Z. Sun, “Efficient DDoS detection based on K-FKNN in software defined networks,” IEEE Access, vol. 7, pp. 160536–160545, 2019. https://doi.org/10.1109/ACCESS.2019.2950945.
    14. H. Zhang, L. Zhou, and J. Lei, “Renyi entropy-based DDoS attack detection in SDN-based networks,” in Proc. 2023 IEEE 3rd International Con-ference on Electronic Technology, Communication and Information (ICETCI), Changchun, China, pp. 334–337, May 2023. https://doi.org/10.1109/ICETCI57876.2023.10176631.
    15. K. Bhushan and B. B. Gupta, “Distributed denial of service (DDoS) attack mitigation in software defined network (SDN)-based cloud computing environment,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, pp. 1985–1997, 2019. https://doi.org/10.1007/s12652-018-0800-9.
    16. Charles Elkan, “Results of the KDD'99 Classifier Learning”, SIGKDD Explorations 1(2): 63-64, 2000. https://doi.org/10.1145/846183.846199.
    17. L. Breiman, “Random Forests”, Machine Learning 45(1):5–32, 2001. https://doi.org/10.1023/A:1010933404324.
    18. Yongguang Zhang, Wenke Lee, and Yi-An Huang, “Intrusion Detection Techniques for Mobile Wireless Networks”, Wireless Networks, Volume 9, Issue 5, September 2003.
    19. Kim, Hyojoon, and Nick Feamster. "Improving network management with software defined networking." IEEE Communications Magazine 51.2 (2013): 114-119. https://doi.org/10.1109/MCOM.2013.6461195.
    20. Zhou, Yuanhao, et al. "A load balancing strategy of SDN controller based on distributed decision." Trust, Security and Privacy in Computing and Communications (TrustCom), 2014 IEEE 13th International Conference on. IEEE, 2014. https://doi.org/10.1109/TrustCom.2014.112.
    21. Yonghong, Fu, et al. "A dormant multi-controller model for software defined networking." China Communications 11.3 (2014): 45-55. https://doi.org/10.1109/CC.2014.6825258.
    22. Karamjeet Kaur1, Japinder Singh2 and Navtej Singh Ghumman, “Mininet as Software Defined Networking Testing Platform”, International Con-ference on Communication, Computing & Systems (ICCCS–2014).
    23. Mininet. http://mininet.org/[M].
    24. Smith, A., et al. (2022). Ethical AI in Software-Defined Networks: Challenges and Frameworks. IEEE Communications Standards Magazine, 6(4), 40–47.
    25. Kiran, M., et al. (2023). AI Governance in SDN Environments: Ensuring Fair and Accountable Traffic Management. ACM Transactions on Internet Technology (TOIT), 23(1), 1–24. https://doi.org/10.1145/3641104.
    26. Jain, S., et al. (2013). B4: Experience with a Globally-Deployed Software Defined WAN. ACM SIGCOMM Computer Communication Review, 43(4), 3–14. https://doi.org/10.1145/2534169.2486019.
    27. AT&T (2020). dNOS: A Disaggregated Network Operating System for Modern SDN Deployments. AT&T Technical White Paper.

  • Downloads

  • How to Cite

    Singh, S., & Sharma , D. K. . (2025). Implementation for Improving SDN Efficiency with The ML Algorithm, RF. International Journal of Basic and Applied Sciences, 14(3), 170-177. https://doi.org/10.14419/yxxp2e46