Business Faculty Publications
Document Type
Article
Publication Date
8-31-2022
Publication Title
Electronics
Keywords
DDoS attack; detection; filter method; wrapper method; clustering algorithms
Disciplines
Business | Management Information Systems
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.
Recommended Citation
Zeinalpour, Alireza and Ahmed, Hassan A., "Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method" (2022). Business Faculty Publications. 357.
https://engagedscholarship.csuohio.edu/bus_facpub/357
DOI
https://doi.org/10.3390/ electronics11172736
Version
Publisher's PDF
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume
11
Issue
2736