Efficient Detection of Different DDoS Attacks using SVM, Random Forest and K-means Classifier

Stanley, Tanya (2022) Efficient Detection of Different DDoS Attacks using SVM, Random Forest and K-means Classifier. Masters thesis, Dublin, National College of Ireland.

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Abstract

Distributed Denial of Service is an attack that tries to overwhelm the victim system or network with malicious traffic, endangering the service's availability. It is known to be one of the most common cyber attacks done on networks, still detecting it at an early stage has not become perfect or accurate. This study proposes three different models, namely Support Vector Machine (SVM), Random Forest Classifier and K-means to detect and classify DDoS attacks and differentiate attack traffic from benign traffic. For the purpose to classify and analyse these models, we have used three datasets each comprising of a different DDoS attack. The models are generated using Principal Component Analysis to determine the essential features and narrow down the dimension of our dataset. The models effectively classify the traffic with respect to its nature i.e., whether it is malicious or not.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	05 Jan 2023 16:00
Last Modified:	05 Jan 2023 16:00
URI:	https://norma.ncirl.ie/id/eprint/6064

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