Detecting and Mitigating Botnet Attacks In Software-Defined Networks Using Deep Learning Techniques

DOI

10.2991/978-94-6463-858-5_243How to use a DOI?

Keywords

Software-Defined Networking; Botnet Detection; Distributed; Denial-of-Service attack; Cybersecurity; Deep Learning Techniques

Abstract

Network administration has been completely transformed by Software-Defined Networking (SDN), which makes it for centralized possible control, programmability, and dynamic resource allocation. However, because it is centralized, it is vulnerable to serious security risks, including DDoS assaults that use botnets to seriously interfere with network operations. Implementing sophisticated intrusion detection systems is crucial since traditional security measures are unable to identify and neutralize these changing threats. This paper proposes a Network Intrusion Detection System that uses deep learning for the real-time recognizing and managing botnet attacks in environments that use SDN settings. Using a new simulation-based dataset, we evaluate how well Recurrent Neural Networks (RNNs), Convolutional Neural Networks(CNNs), Deep Neural Networks (DNNs), Multilayer Perceptrons (MLP), and Long Short-Term Memory (LSTM) perform in classification. Feature weighting and threshold adjustment approaches are used to increase computational efficiency and detection accuracy. The study emphasizes how important collection of features and hyper parameter tuning are for enhancing the detection’s resilience. To make SDN systems even more secure, future studies can investigate hybrid DL models. By offering a robust defense, this research advances AI-driven security solutions.

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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TY  - CONF
AU  - G. Ravi Kumar
AU  - Vijayagiri Amulya
AU  - Abhay Pratap Singh
AU  - Karra Vinay Reddy
PY  - 2025
DA  - 2025/11/04
TI  - Detecting and Mitigating Botnet Attacks In Software-Defined Networks Using Deep Learning Techniques
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2888
EP  - 2903
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_243
DO  - 10.2991/978-94-6463-858-5_243
ID  - Kumar2025
ER  -