Enhancing DDoS Attack Detection via Blending Ensemble Learning

This research focuses on identifying DDoS attacks using an ensemble learning approach that incorporates blending techniques. We developed an innovative methodology by selecting the 21 most significant features from the CIC-DDoS2019 dataset. To improve classification accuracy, we used a two-layer blending ensemble technique. In the first layer, we combined Decision Tree, Logistic Regression, and KNN classifiers, while the second layer used a Random Forest classifier. The model achieved exceptional results, with a 99.94% accuracy score and a 97.35% F1 score for detecting DDoS attacks accurately. We also created a user-friendly web portal to make the model accessible for individuals in network security, regardless of their technical expertise. This approach advances DDoS attack detection and enhances usability for users in the field of network security.