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Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach

Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach

Anupama Mishra, Bineet Kumar Joshi, Varsha Arya, Avadhesh Kumar Gupta, Kwok Tai Chui
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 10
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.309707
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MLA

Mishra, Anupama, et al. "Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach." IJSSCI vol.14, no.1 2022: pp.1-10. http://doi.org/10.4018/IJSSCI.309707

APA

Mishra, A., Joshi, B. K., Arya, V., Gupta, A. K., & Chui, K. T. (2022). Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-10. http://doi.org/10.4018/IJSSCI.309707

Chicago

Mishra, Anupama, et al. "Detection of Distributed Denial of Service (DDoS) Attacks Using Computational Intelligence and Majority Vote-Based Ensemble Approach," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-10. http://doi.org/10.4018/IJSSCI.309707

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Abstract

The term “distributed denial of service” (DDoS) refers to one of the most common types of attacks. Sending a huge volume of data packets to the server machine is the target of a DDoS attack. This results in the majority of the consumption of network bandwidth and server, which ultimately leads to an issue with denial of service. In this paper, a majority vote-based ensemble of classifiers is utilized in the Sever technique, which results in improved accuracy and reduced computational overhead, when detecting attacks. For the experiment, the authors have used the CICDDOS2019 dataset. According to the findings of the experiment, a high level of accuracy of 99.98% was attained. In this paper, the classifiers use random forest, decision tree, and naïve bayes for majority voting classifiers, and from the results and performance, it can be seen that majority vote classifiers performed better.

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