A Machine Learning Classification Model Using Random Forest for Detecting DDoS Attacks
Thai Son Chu, Weisheng Si, Simeon J. Simoff, Quang Vinh Nguyễn · 2022 International Symposium on Networks, Computers and Communications (ISNCC) · 2022
Distributed Denial of Service (DDoS) attacks exhaust the resources of network services by generating a huge volume of network traffic. They constitute a primary threat to the current Internet community. To mitigate this threat, we propose a Machine Learning model based on Random Forest for detecting DDoS attacks. In our Random Forest, a great number of decision trees under both the Gini Index and Entropy criteria are constructed to improve the detection accuracy. Moreover, with the intrinsic simplicity of Random Forest model, our model is fast in terms of model convergence and attack detection. The data for building our model comes from the newly released dataset CICDDoS2019, which contains a large variety of DDoS attacks with a new classification based on network flows. The experimental results show that our model achieves high accuracy rates and F1_scores, and outperforms an existing state-of-the-art machine learning model for DDoS detection.