Novel AI-Driven Approach to cloud intrusion detection using Boruta and PSO feature selection

Srinivasan, Tarankaarthikeshwar (2025) Novel AI-Driven Approach to cloud intrusion detection using Boruta and PSO feature selection. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (1MB) | Preview

Abstract

Nowadays, cloud computing is essential part of modern digital infrastructure, it is targeted by many different types of cyber-attacks. For these environments to be secure, intrusion detection systems (IDS) must be quick and effective. This study presents a scalable and reliable IDS architecture that uses sophisticated feature engineering and data simplification to improve detection accuracy. To decrease complexity, the datasets of CICIDS 2017 which contains comprehensive assault labels, are first categorized into more general, understandable groups. To save only the most pertinent features, a hybrid feature selection method that combines Boruta and Particle Swarm Optimization (PSO) is utilized. The Synthetic Minority Over-Sampling Technique (SMOTE) is used to address the issue of class imbalance. Several machine learning models, such as Random Forest, LightGBM, and XGBoost, are subsequently trained using the improved dataset.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, Michael UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence 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
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	19 Aug 2026 15:25
Last Modified:	19 Aug 2026 15:25
URI:	https://norma.ncirl.ie/id/eprint/9553

Actions (login required)

View Item