ARCHIVES

DDoS network attacks are referred to as Distributed Denial of Service attacks. When theDDoS Attack occurs on a particular server it makes the server slow down and even crashessometimes. The attacker uses the HTTP requests to overwhelm the server which is consistent with itsprocess. Because of DDoS, the user’s site shows the service will not be provided and is denied. Inthe existing research study, the authors worked on Machine Learning Algorithm which had very low accuracy. It is necessary to work with the latest dataset and algorithms with greater accuracy toidentify the current state of DDoS attacks. We used a machine learning approach for DDoS attackswhich is Classification and Prediction. For this purpose, we used the Supervised Machine Learning Algorithms which are SVM and Naïve Bayes. For the proposed study, UNSWnb15 and CCIDS2017dataset was used. Additionally, we generated a confusion matrix for the identification of the modelperformance. The Machine Learning approach is used to predict a DDoS in a network with amaximum accuracy of 99.68%, if the recommended combination of feature selection and classification algorithm is chosen.