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A Comparative Analysis of Software-Defined Networking (SDN) Security Policies With and Without Reinforcement Learning Automation

Authors

  • Syed Shabeeb Raza

    WeUno Technologies, Karachi, Pakistan
    Author

  • Muhammad Imran Saleem

    Sir Syed University of Engineering & Technology, Karachi, Pakistan
    Author

  • Hassan Ahmed Khan

    Iqra University, Karachi, Pakistan
    Author

  • Shaheena Noor

    Sir Syed University of Engineering & Technology, Karachi, Pakistan
    Author

  • Ali Yousuf Khan

    Sir Syed University of Engineering & Technology, Karachi, Pakistan
    Author

  • Eman Razzaq

    Sir Syed University of Engineering & Technology, Karachi, Pakistan
    Author

DOI:

https://doi.org/10.22581/0413

Keywords:

Policy Automation , Network Security, Artificial Intelligence, Dynamic policy management , Software-Defined Networking (SDN), Reinforcement Learning (RL)

Abstract

Software-Defined Networking (SDN) has changed the way networks are built in modern days by separating the control and data planes which thus made it possible to have centralized programmability and flexibility. Although these security mechanisms have developed, SDN security management still depends on static or manually configured policies which are not sufficient anymore for the dynamic and sophisticated cyber threats. AI, especially, Reinforcement Learning (RL), has now become a reliable option to automate SDN security policies and thus allow quick adaptive response to changing threats. In this article, a complete theoretical analysis has been conducted where SDN security policy management through RL-based dynamic automation is juxtaposed against traditional methods. By combining recent studies outcomes, the current work delineates the pros, cons, and practical applicability of both methods. A proposal of a conceptual framework for the implementation of RL into SDN controllers is made which would subsequently help to connect the present gaps and guide future research in both academic and industrial sectors. The paper wraps up with the identification of research challenges, posing of questions that need to be answered, and giving suggestions for the further development of automated, intelligent SDN security architectures

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Published

2026-04-01

Issue

Vol. 45 No. 2 (2026): April Issue

Section

Articles

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Copyright (c) 2026 Mehran University Research Journal of Engineering and Technology

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

A Comparative Analysis of Software-Defined Networking (SDN) Security Policies With and Without Reinforcement Learning Automation. (2026). Mehran University Research Journal of Engineering and Technology, 45(2), 19-36. https://doi.org/10.22581/0413

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