Classification of Distributed Denial of Service Attacks Using Machine Learning Methods

Year 2024, , 15 - 20, 29.06.2024

Uğur İnce , Gülşah Karaduman

https://doi.org/10.46572/naturengs.1450965

Abstract

With the digitalized world, the uninterrupted provision of services over the internet, especially in hospitals, banking, energy, etc. systems is of great importance. There are many attack methods to disrupt or disable these services. Denial of service attacks, which are one of these methods, are more complex and difficult to detect; Organizing such attacks becomes very easy and cost-effective thanks to many tools. Attackers can perform DDoS attacks on target systems with very little knowledge and skills, and they can render target systems inoperable, sometimes for a short time or for days. This work aims to use machine learning techniques to classify Ddos attacks with high accuracy. The CIC-DDoS2019 dataset, which is the most up-to-date and comprehensive attack dataset on the internet, was used. The data obtained after various data preprocessing processes were classified by machine learning methods and the accuracy rates, recall, f1-score and precision values in these methods were compared.

Keywords

Distributed Denial of Service Attacks, Machine Learning, Denial of Service Attacks

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