A Comparative Analysis of FCNN, XGBoost, and Random Forest Models for DDoS Attack Detection on Software Defined Networking

The threat of Distributed Denial of Service (DDoS) attacks is a serious challenge to network stability and security, especially in centralized Software Defined Networking (SDN) architectures. This study aims to evaluate and compare the performance of three classification algorithms in detecting DDoS attacks, namely Fully Connected Neural Network (FCNN), Extreme Gradient Boosting (XGBoost), and Random Forest. The dataset used is public SDN network traffic data that has gone through a normalization and feature selection process to improve model efficiency and accuracy. The evaluation was conducted on four scenarios of training and test data sharing ratios$(90: 10,80: 20,70: 30$, and$60: 40)$using accuracy, precision, recall, and$\text{F 1}$-score metrics. The test results show that Random Forest obtained the highest accuracy of 99.61 %, followed by FCNN at 94.95 %, and XGBoost at 99.01 %. Despite the slightly lower accuracy, XGBoost showed the best computational efficiency with the fastest training time of 0.94 seconds and the fastest prediction time of 0.03 seconds, making it an ideal choice for real-time detection system implementation. These findings contribute to the development of a precise, efficient, and adaptive DDoS detection system. Future research is recommended to explore advanced feature selection methods such as Recursive Feature Elimination (RFE) to improve the overall performance and efficiency of the model.