| Issue |
ITM Web Conf.
Volume 82, 2026
International Conference on NextGen Engineering Technologies and Applications for Sustainable Development (ICNEXTS’25)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 6 | |
| Section | Communication and Networking | |
| DOI | https://doi.org/10.1051/itmconf/20268202001 | |
| Published online | 04 February 2026 | |
AI-Based Secure SDN Framework for Smart City IoT Networks
1 Department of ECE, Velalar College of Engineering and Technology, Erode, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
2 Department of ECE, Velalar College of Engineering and Technology, Erode, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
3 Department of ECE, Velalar College of Engineering and Technology, Erode, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
4 Department of ECE, Velalar College of Engineering and Technology, Erode, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
5 Department of ECE, Velalar College of Engineering and Technology, Erode, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
6 Department of ECE, Velalar College of Engineering and Technology, Erode, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
The purpose of this project is to design an AI-Based Secure Software Defined Networking (SDN) Framework for Smart City IoT Networks to provide intelligent traffic management, real-time threat detection, and improved security for IoT devices that can communicate with each other. This project suggests a methodology that utilizes AI methods into SDN to improve the network's dynamic ability for identifying and mitigating cyberattacks. A hybrid security solution is utilized that uses both Rule-Based Detection mechanisms and Machine Learning (ML) Based Detection techniques. The Rule-Based Detection mechanisms make use of predefined rules and thresholds to recognize malicious activity, and a ML algorithm employs trained models to recognize sophisticated and unknown threats with high precision. The framework itself is realized in an SDN setup and also emulated through MATLAB software to analyze performance in various network attack situations. The outcomes show that the Rule-Based Detection registered an accuracy of 98.285% for well-known attack patterns, and the ML Based Detection was realized at a perfect degree of accuracy (100%) with the aim of efficient identification and classification of malicious network behavior. Overall, the AI based SDN framework integrates.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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