A Comparison of Six Machine Learning Techniques for Cloud DDoS Attack Detection - Boonsiri, Thanadoln (Applied Information Technology And Computer Science, 2023)
Cloud computing is a network access approach that allows for convenient, limitless, ondemand network access to a public computer resource pool. The DDoS assault is one ofthe most serious risks to cloud users since it jeopardizes cloud providers' services andrenders them inaccessible to legitimate clients. Machine learning approaches are capableof detecting DDoS assaults as well as preventing them. CICDDoS2019 dataset is used inthis work. This research involves four phases which are pre-processing, feature selection,classification and parameter evaluation. The six machine machine learning techniquesimplemented in this research are Logical Regression, Random Forest, Support VectorMachines, Decision Tree, Naive Bayes and K-Nearest Algorithms. To evaluate theeffectiveness of each machine learning algorithm, accuracy, precision and recall are theparameters used. It is found that Random Forest, Support Vector Machines and K-NearestAlgorithms produce the best results in terms of accuracy, precision and recall.