International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Proactive SDN Defense System Using Machine Learning

Mitali UphadeStudent, Information Technology, PVG College of Engineering and S. S. Dhamankar Institute of Management, Nashik, Maharashtra, IndiaPuja KateStudent, Information Technology, PVG College of Engineering and S. S. Dhamankar Institute of Management, Nashik, Maharashtra, IndiaAniket SangaleStudent, Information Technology, PVG College of Engineering and S. S. Dhamankar Institute of Management, Nashik, Maharashtra, IndiaProf. Prasad. A. LahareAssistant Professor, Information Technology, PVG College of Engineering and S. S. Dhamankar Institute of Management, Nashik, Maharashtra, India

Vol 8 No 4 (2024): Volume 8, Issue 4, April 2024 | Pages: 269-274

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 19-05-2024

doi Logo doi.org/10.47001/IRJIET/2024.804041

pdf iconFull Text PDF
Abstract

The evolving threat landscape in cybersecurity demands a proactive approach that anticipates and thwarts cyberattacks before they can inflict damage. This paper introduces a novel Proactive SDN Defence System powered by machine learning (ML), aiming to revolutionize network security by: Harnessing the dynamic control capabilities of Software- Defined Networks (SDN): OpenFlow protocols enable real-time network reconfiguration based on ML predictions. Leveraging the predictive power of ML: Advanced algorithms like SVMs, LSTMs, and RNNs analyze network data to identify anomalies, predict imminent threats, and trigger proactive countermeasures.

Keywords

OpenDaylight, Anomaly, Algorithm, SHA, Algorithm, Scikit-learn, Texting, Search, Requirements


Citation of this Article

          

Mitali Uphade, Puja Kate, Aniket Sangale, Prof. Prasad. A. Lahare, “Proactive SDN Defense System Using Machine Learning”, Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 8, Issue 4, pp 269-274, April 2024. Article DOI https://doi.org/10.47001/IRJIET/2024.804041

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Anomaly Detection for Proactive SDN Defense: A Survey": Y. Li et al., IEEE Communications Surveys & Tutorials (2022).
  2. Machine Learning-based Proactive Security Techniques for Software-Defined Networks: A Survey": M. A. Khan et al., IEEE Access (2022).
  3. Proactive Defense Mechanisms in SDN: A Survey": S. Sharma et al., Computer Networks (2022).
  4. Machine Learning-based Proactive Defense System for SDN against Cyberattacks": M. A. Khan et al., International Journal of Computer Network and Information Security (2022).
  5. Proactive Anomaly Detection and Mitigation System for SDN using Machine Learning": J. Wang et al., IEEE Transactions on Network and Service Management (2021).
  6. Machine Learning-based Proactive Defense Framework for SDN-based Networks": P. K. Mishra et al., Journal of Network and Computer Applications (2021).
  7. A Machine Learning-driven Proactive Defense System for DDoS Attacks in SDN-based Networks": F. Hu et al., Future Generation Computer Systems (2021).
  8. Proactive Defense Framework for SDN Networks against Botnet Attacks using Machine Learning": A. Al-Dhubaihi et al., Journal of Network and Computer Applications (2020).
  9. Anomaly Detection and Mitigation for Proactive Defense in SDN: A Reinforcement Learning Approach": Y. Zhang et al., IEEE Transactions on Network and Service Management (2020).
  10. Machine Learning-based Proactive Defense System for SDN against Zero-day Attacks": A. A. A. Al-Ameen et al., Journal of Network and Computer Applications (2020).
  11. A Federated Learning-Based Proactive SDN Defense Framework for DDoS Mitigation in IIoT Systems": Y. Zhou et al., IEEE Transactions on Industrial Informatics (2023).
  12. Towards Proactive and Efficient DDoS Mitigation in IIoT Systems: A Moving Target Defense Approach": Yuyang Zhou et al., IEEE Transactions on Industrial Informatics (2021).
  13. A Federated Learning-Based Proactive SDN Defense Framework for DDoS Mitigation in IIoT Systems": Y. Zhou et al., IEEE Transactions on Industrial Informatics (2023).
  14. Towards Proactive and Efficient DDoS Mitigation in IIoT Systems: A Moving Target Defense Approach": Yuyang Zhou et al., IEEE Transactions on Industrial Informatics (2021).
  15. An SDN-enabled Proactive Defense Framework for DDoS Mitigation in IoT Networks": X. Chen et al., IEEE Transactions on Network and Service Management (2023).
  16. Proactive SDN Defense against DoS Attacks Using Machine Learning": P. K. Mishra et al., Journal of Network and Computer Applications (2023).
  17. Dynamic Network Reconfiguration for Proactive SDN Defense using Reinforcement Learning": S. Sharma et al., Journal of Systems and Software (2023).
  18. Machine Learning-based Anomaly Detection and Mitigation in Software Defined Networks (SDN)": M. Khan et al., IEEE Access (2023).
  19. Proactive SDN Defense System for Botnet Detection and Mitigation using Machine Learning": R. Kumar et al., International Conference on Computer Networks and Information Technology (2023).
  20. Proactive SDN Defense System for Denial-of-Service (DoS) Attacks in Cloud Computing Environments": M.A. Al-Ameen et al., Journal of Network and Computer Applications (2023).
  21. Proactive SDN Defense for Web Applications using Anomaly Detection and Machine Learning": N. Singh et al., International Conference on Information Systems and Technology (2023).
  22. Proactive Attack Prediction and Mitigation Framework for SDN-based IoT Networks": Y. Zhang et al., IEEE Internet of Things Journal (2023).

Copyright @ 2026 IRJIET. All Right Reserved.

Licensed underCreative Commons Attribution 4.0 International License.