Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method

Abstract

The curse of dimensionality, due to lots of network-traffic attributes, has a negative impact on machine learning algorithms in detecting distributed denial of service (DDoS) attacks. This study investigated whether adding the filter and wrapper methods, preceded by combined clustering algorithms using the Vote classifier method, was effective in lowering the false-positive rates of DDoS-attack detection methods. We examined this process to address the curse of dimensionality of machine learning algorithms in detecting DDoS attacks. The results of this study, using ANOVA statistical analyses, showed that incorporating the wrapper method had superior performance in comparison with the filter and clustering methods. IT professionals aim at incorporating effective DDoS-attack detection methods to detect attacks. Therefore, the contribution of this study is that incorporating the wrapper method is the most suitable option for organizations to detect attacks as illustrated in this study. Subsequently, IT professionals could incorporate the DDoS-attack detection methods that, in this study, produced the lowest false-positive rate (0.012) in comparison with all the other mentioned studies.