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Text is converted into numerical vectors using TF-IDF, and LightGBM is identified as the most effective model, outperforming Logistic Regression and Naive Bayes in experiments. 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All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nJecinta Ifechukwu Fidelis  \nSignature:    \n16/09/2024  \nDate:    \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nProtection Against Spear Phishing Attacks Using the Ensemble Method of Machine Learning  \nJecinta Ifechukwu Fidelis  \n23148306  \nAbstract  \nThis 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.  \nThe 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.  \nThe 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.  \nKey words: Ensemble Machine L","cbCaiebFFvkEFgeQ","https://ap.wps.com/l/cbCaiebFFvkEFgeQ","pdf",1261839,36,"English","# Abstract\n# 1 Introduction\n## Phishing and attack context\n## Types and categories of phishing","[{\"question\":\"What problem does the project address?\",\"answer\":\"The project targets the difficulty of detecting and classifying spear phishing emails, which are designed to appear legitimate and trick recipients into revealing sensitive information.\"},{\"question\":\"How is email content represented for the models?\",\"answer\":\"The study converts email text into numerical vectors using TF-IDF feature extraction.\"},{\"question\":\"Which model performs best and how is it deployed?\",\"answer\":\"LightGBM performs best in the reported experiments. It is deployed with a user-friendly Gradio interface for real-time email classification.\"}]","Protection Against Spear Phishing Attacks Using the Ensemble Method of Machine Learning - MSc Project Submission Sheet | PDF",91]