LDA-ML: A Hybrid DDoS Detection Attacks in SDN Environment using Machine Learning

Rezaei, Alireza; Amini, Amineh

doi:10.48308/jicse.2025.239842.1075

LDA-ML: A Hybrid DDoS Detection Attacks in SDN Environment using Machine Learning

Document Type : Original Article

Authors

alireza rezaei

amineh Amini

Department of Computer Engineering Karaj Branch, Islamic Azad University Karaj, Iran

https://doi.org/10.48308/jicse.2025.239842.1075

Abstract

Abstract—In today's world, DDoS attacks are becoming more common and complex; thus, they constitute a great challenge for network security under the auspices of SDN. The research effort described here proposes an integrated hybrid model called "LDA-ML," which leverages some state-of-the-art machine learning methods: LDA, naive bayes, random forest, and logistic regression. We optimize the data analysis process by leveraging LDA for feature selection and dimensionality reduction, followed by a sequential application of the classifiers to exploit their strengths. Evaluated on the CICDDoS-2019 dataset, the proposed model has achieved an outstanding accuracy of 98.98%, indicating the efficacy of the model in correctly classifying benign versus attack traffic. All of the above underlines the robustness of the proposed LDA-ML model, pointing to great potential for its application to continuously improve cybersecurity strategies against DDoS threats in SDN architectures. This holistic approach offers improvements in detection, while it also enriches diagnostic insights-an important contribution to finding effective security solutions in increasingly dynamic network environments.

Keywords

Keywords— DDoS attack

SDN

Cybersecurity

Machine learning

Subjects