Feature-Driven Supervised Learning for Detecting DDoS Attack

Md Boktiar Hossain 1, *, Rashedur Rahman 2 and Khandoker Hoque 3

1 Department of Information and Communication Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh.
2 Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
3 Department of Electrical and Electronic Engineering, Brac University, Dhaka, Bangladesh.
 
Research Article
International Journal of Science and Research Archive, 2021, 04(01), 393-402.
Article DOI: 10.30574/ijsra.2021.4.1.0204
Publication history: 
Received on 21 October 2021; revised on 23 November 2021; accepted on 28 November 2021
 
Abstract: 
Distributed Denial-of-Service (DDoS) attacks are intentional efforts to disrupt the normal traffic of a targeted server, network, or organization by overwhelming the victim or its neighboring systems with excessive network traffic. Detecting such attacks using machine-learning models is challenging due to significant variations in traffic patterns and rates. So, an automated detection approach is proposed, which reduces the feature space to minimize model overfitting and computational cost. The CICDDoS2019 dataset, including a wide range of DDoS attack scenarios, is used to train and evaluate the proposed method in a cloud-based environment. Relevant features are extracted using the Extra Trees classifier and then passed to Decision Tree, XGBoost, and Random Forest classifiers. XGBoost achieved the highest validation accuracy of 98.87% with feature selection, while Decision Tree maintained a strong baseline accuracy of 98.49% even without feature selection.
 
Keywords: 
CICDDoS2019 dataset; DDoS attacks; Data preprocessing; Feature selection; Classification
 
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0