Strategic recommendations for enhancing DDoS defense mechanisms in cloud environments
Chisom Elizabeth Alozie · 2025
As presented and motivated in the introduction of this project study, a DDoS detection system for a cloud environment aided by a machine learning modem technique was implemented and a comparative analysis modem was conducted. The CICFlowMeter was used to extract the new dataset to CSV format which includes obtaining the proper flow features for the model building. Furthermore, feature selection using person correlation coefficient improved the accuracy performance of the ML models training with Random Forest, Support Vector Machine, Decision Tree, and K-Nearest Neighbors achieving a rate of 100% accuracy, precision, recall and F1 score except for Naive Bayes with a 98% accuracy, 97% precision, 99% recall and 98% F1 score. Also, the open-source dataset performs very well with RF, DT and KNN achieving an accuracy of 100%, SVM 95% and NB 99%. Overall, the new dataset outperforms the open-source dataset with an accuracy score of 99.6% while the benchmark achieved 98.8%. Based on the results achieved, all the models selected, the new datasets and the open-source dataset used for this study are ideal models and datasets for intrusion detection.