Prediction of Denial of Service Attack using Machine Learning Algorithms

Abstract

DDoS attack is one of the significant security threats in today’s Internet world. The main intention of the network thread is to make the resource unavailable such as flooding attacks. Here, Machine learning algorithms have been used for detecting DDoS attacks. Generally, the success of any algorithm has depended on the selection of appropriate data sets and the identification of attack parameters. The KDD-CUP dataset has been taken for a detail investigation of the DDoS attack. The K-nearest neighbor, ID3, Naive Bayes and C4.5 algorithms are compared in a single platform concluding with the positives with Naive Bayes. The main objective of the paper is to compare and predict the error rate, computation time, Accuracy of the algorithms using the Tanagra tool. Finally, these correlative algorithms have been compared and verified through experimental verification and graphical representation.