Comparison of the Use of Support Vector Machine (SVM) & Random Forest Algorithms (RF) for DDOS Attack Detection

Ho Zi Rui, Tan Ying Chien, Loo Xin Ee, Loo Xin Ee and Law Teng Yi
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Ho Zi Rui: New Era University College
Tan Ying Chien: New Era University College
Loo Xin Ee: New Era University College
Loo Xin Ee: New Era University College
Law Teng Yi: New Era University College

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 1, 1126-1138

Abstract: DDoS attack is one of the major challenges to network security in today’s time, destroying services and creating huge losses. The study here presents an assessment of the performance of Support Vector Machine and Random Forest algorithms on DDoS detection based on the DDoS-SDN datasets. Key metrics that were considered for performance evaluation include accuracy, precision, recall, and F1-score. The results indicate that RF outperforms SVM in complex, high-dimensional datasets such as DDoS-SDN, using its ensemble learning approach to attain greater robustness and accuracy. This research also explores the role of feature selection techniques, such as Genetic Algorithm (GA) and Recursive Feature Elimination (RFE), to enhance model efficiency and accuracy. This paper discusses the strengths and limitations of both algorithms to provide insight into the optimization of machine learning models toward efficient DDoS detection for secure and resilient network systems.

Date: 2025
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