Evaluation of Classification algorithms for Distributed Denial of Service Attack Detection

Abstract

Distributed Denial of Service (DDoS) attacks aims exhausting the target network with malicious traffic, which is a threat to the availability of the service. Many detection systems, specifically Intrusion Detection System (IDS) have been proposed throughout the last two decades as the Internet evolved, although users and organizations find it continuously challenging and defeated while dealing with DDoS. Though, IDS is the first point of defense for protecting critical networks against ever evolving issues of intrusive activities, however it should be up to date all the time to detect any anomalous behavior so that integrity, confidentiality and availability of the service can be preserved. But, the accuracy of new detection methods, techniques, algorithms heavily rely on the existence of well-designed datasets for training purposes and evaluation by creating the classifier model. In this work, experimentation has been carried out using major supervised classification algorithms to classify the DDoS attack accurately from the legitimate flows. Among all the classifier, tree-based classifiers and distance-based classifiers performed the best.