Protection Against Spear Phishing Attacks Using the Ensemble Method of Machine Learning

Fidelis, Jecinta Ifechukwu (2024) Protection Against Spear Phishing Attacks Using the Ensemble Method of Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study investigated and implemented the use of multiple ensemble machine learning methods to enhance the detection and classification of spear phishing emails, which is a critical challenge in cybersecurity due to the sophisticated nature of phishing attacks. The primary motivation for this study is the need for more effective phishing detection systems that can accurately distinguish between legitimate emails and spear phishing attempts beyond the human eyes. To address this issue, the study focused on the following three key objectives: enhancing feature extraction techniques, implementing ensemble machine learning models, and deploying a practical phishing detection system. The research employed advanced text feature extraction methods with specific regard to Term Frequency-Inverse Document Frequency (TF-IDF), to convert email content into numerical vectors, thereby improving model accuracy. In this study, LightGBM emerged as the most effective model in test experiments and outperforming traditional models like Logistic Regression and Naive Bayes. To ensure practical applicability, this model was deployed with a user-friendly interface developed using Gradio, enabling real-time email classification. This integration provides a practical solution for organisations to combat spear phishing attacks. The study's findings demonstrate the efficacy of ensemble models in improving phishing detection and offer significant implications for both academic research and practical applications. Future work will explore adaptive learning approaches to further enhance the system's resilience against evolving phishing tactics and address limitations identified in dynamic environments.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Khan, Imran UNSPECIFIED
Uncontrolled Keywords:	Ensemble Machine Learning; Spear Phishing Detection; Email Classification; LightGBM; Cybersecurity
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 Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	17 Jul 2025 14:48
Last Modified:	17 Jul 2025 14:48
URI:	https://norma.ncirl.ie/id/eprint/8172

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