Novel Approaches for Real-Time Detection of DDoS Attacks in Cloud Computing Environments Using Advanced Machine Learning Techniques

Vinnamuri, Venkata Sai Charan (2024) Novel Approaches for Real-Time Detection of DDoS Attacks in Cloud Computing Environments Using Advanced Machine Learning Techniques. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (546kB) | Preview
Preview	PDF (Configuration Manual) Download (623kB) | Preview

Abstract

This research focuses on the identification of DDoS attacks in cloud computing systems using more sophisticated machine learning schemes. Standard security mechanisms have failed to provide adequate protection in relation to modern, large-scale and complex DDoS attacks that cause substantial service availability and monetary disadvantages. The proposed methodology involved building and comparing RF, SVM, and the RF-SVM combined models in order to improve the detection performance. Stage one of data preprocessing involved feature selection and normalization while stage two of data preprocessing involved training and evaluation of the models using network traffic data. The RF model proved consistent for the choice of estimating Rf classification, high accuracy, low chance of overfitting due to the use of the ensemble learning method. The SVM model was also good especially in high dimensionality, but the recall value was slightly down by approximately one because of misclassification. The application of the hybrid model was the most effective producing 100% accuracy, precision, recall, and F1 score with least latency and highest throughput that made it suitable for real time detection. Consequently, these outcomes achieve the research aims and objectives to develop a reliable, efficient, and accurate DDoS detection system for dynamic cloud infrastructure to bolster cloud service security and reliability.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kumar Sharma, Jitendra UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	04 Jul 2025 11:40
Last Modified:	04 Jul 2025 11:40
URI:	https://norma.ncirl.ie/id/eprint/8062

Actions (login required)

View Item