Abstract:
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.
