Construct an Efficient DDoS Attack Detection System Based on RF-C4.5-GridSearchCV

Distributed Denial of Service (DDoS) attacks are intended to drain network resources and increase malicious traffic. This paper describes the mechanism and methods of DDoS attacks. By studying and analyzing a data set of this type, more than one parameter is collected, preprocessed, and expanded. In this work, experiments were carried out by building RF and C4.5 classification models and using the GridSearchCV class for the purpose of optimizing the parameters of these techniques are used. Then, an Extra tree classifier is used to identify the feature and reduce the dimensions according to the importance of each feature, eliminating the noise value that occurs in the feature data. Use the accuracy of the confusion matrix to illustrate the evaluation of the model, and finally check that the evaluation model is effective and can be trusted.