 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: February 2026
Volume 11 | Issue 2
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21303
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
Total Countries : 156
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | A Survey on the Integration of Machine Learning Techniques in Software-Defined Networking |
| Authors Name: | N. SENTHILKUMARAN , Dr. R. SANKARASUBRAMANIAN |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_208317
|
| Published Paper Id: | IJRTI2512033 |
| Published In: | Volume 10 Issue 12, December-2025 |
| DOI: | |
| Abstract: | Abstract—Software-Defined Networking (SDN) represents a fundamental paradigm shift in modern communication networks, offering extraordinary capabilities through its principles of programmability, centralized control, and abstraction. This innovative architecture fundamentally transforms how network infrastructures are managed and operated, moving away from static, hardware-centric configurations towards dynamic, software-driven orchestration. However, the inherently dynamic, complex, and evolving nature of contemporary SDN environments, coupled with ever-increasing demands for enhanced efficiency, resilience, and adaptability, has led to a increasing interest in applying advanced Machine Learning techniques. This powerful integration leverages ML's analytical prowess to address critical networking challenges such as intelligent traffic classification, robust intrusion detection and prevention, optimal resource allocation, and proactive fault management. This survey provides a comprehensive overview of recent ML approaches rigorously applied within SDN paradigms. It meticulously categorizes various proposed solutions based on their primary application domains (e.g., security, QoS), the families of Machine Learning models employed (e.g., supervised, unsupervised, deep learning), the datasets utilized for training and evaluation, and the key performance metrics reported in academic literature. Furthermore, this paper critically examines the prevalent challenges and open issues that impede the widespread and successful integration of ML in SDN, including concerns related to scalability in large-scale deployments, the scarcity of realistic and labelled network datasets, and the inherent security vulnerabilities of ML models themselves (e.g., adversarial attacks). Finally, it outlines several promising potential research directions, such as federated learning, lightweight ML models, and Explainable AI, that aim to overcome these obstacles and foster the development of smarter, more resilient, and truly self-optimizing network architectures for the future. |
| Keywords: | Index Terms—SDN, paradigm, QoS, Security, ML and self-optimizing, supervised and unsupervised learning |
| Cite Article: | "A Survey on the Integration of Machine Learning Techniques in Software-Defined Networking", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.a249-a260, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512033.pdf |
| Downloads: | 000187 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2512033
Registration ID:208317
Published In: Volume 10 Issue 12, December-2025
DOI (Digital Object Identifier):
Page No: a249-a260 Country: Erode, Tamil Nadu, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2512033 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2512033 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||