Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing

Fray L. Becerra-Suarez (), Ismael Fernández-Roman and Manuel G. Forero ()
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Fray L. Becerra-Suarez: Professional School of Systems Engineering, Faculty of Engineering, Architecture and Urban Planning, Universidad Señor de Sipán, Chiclayo 14000, Lambayeque, Peru
Ismael Fernández-Roman: Professional School of Systems Engineering, Faculty of Engineering, Architecture and Urban Planning, Universidad Señor de Sipán, Chiclayo 14000, Lambayeque, Peru
Manuel G. Forero: Professional School of Systems Engineering, Faculty of Engineering, Architecture and Urban Planning, Universidad Señor de Sipán, Chiclayo 14000, Lambayeque, Peru

Mathematics, 2024, vol. 12, issue 9, 1-14

Abstract: The early and accurate detection of Distributed Denial of Service (DDoS) attacks is a fundamental area of research to safeguard the integrity and functionality of organizations’ digital ecosystems. Despite the growing importance of neural networks in recent years, the use of classical techniques remains relevant due to their interpretability, speed, resource efficiency, and satisfactory performance. This article presents the results of a comparative analysis of six machine learning techniques, namely, Random Forest (RF), Decision Tree (DT), AdaBoost (ADA), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and Dense Neural Network (DNN), for classifying DDoS attacks. The CICDDoS2019 dataset was used, which underwent data preprocessing to remove outliers, and 22 features were selected using the Pearson correlation coefficient. The RF classifier achieved the best accuracy rate (99.97%), outperforming other classifiers and even previously published neural network-based techniques. These findings underscore the feasibility and effectiveness of machine learning algorithms in the field of DDoS attack detection, reaffirming their relevance as a valuable tool in advanced cyber defense.

Keywords: cybersecurity; DDoS attacks; CICDDoS2019 dataset; attack detection; data preprocessing; feature selection; outlier removal; interpretability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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