Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)

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Machine Learning based Security Solutions against DDoS Attacks in Software Defined Networks

Authors
Sanjay Vidhani1, 2, *, Amarsinh Vidhate3
1Department of Computer Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
2Faculty of K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai, India
3Department of Computer Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
*Corresponding author. Email: sanjayvidhani@somaiya.edu
Corresponding Author
Sanjay Vidhani
Available Online 19 April 2025.
DOI
10.2991/978-94-6463-700-7_36How to use a DOI?
Keywords
Software-Defined Networking; Machine Learning; DDoS attacks; Support Vector Machine; Random Forest; Naïve Bayes
Abstract

Software-Defined Networking (SDN) is a revolutionary network paradigm that enhances the flexibility, scalability, and management of networks. However, this centralized approach to network control also creates potential vulnerabilities that attackers can exploit. Traditional security measures are often ineffective in detecting novel and sophisticated attacks. This paper examines the application of various Machine Learning (ML) techniques, including Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Decision Trees (DT), K-Means, DBSCAN, and Agglomerative Clustering, for detecting and mitigating security vulnerabilities in SDN environments. The effectiveness of these models is assessed using network traffic datasets, demonstrating their capability in real-time SDN defense. In future work, Deep Learning (DL) techniques will be explored due to their significant potential to improve the detection and mitigation of Distributed Denial of Service (DDoS) attacks in SDN systems.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
Series
Advances in Intelligent Systems Research
Publication Date
19 April 2025
ISBN
978-94-6463-700-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-700-7_36How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sanjay Vidhani
AU  - Amarsinh Vidhate
PY  - 2025
DA  - 2025/04/19
TI  - Machine Learning based Security Solutions against DDoS Attacks in Software Defined Networks
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 462
EP  - 475
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_36
DO  - 10.2991/978-94-6463-700-7_36
ID  - Vidhani2025
ER  -