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Ensemble Classification Method for Email Spam Prediction

Udogwu, Chinedu Timothy (2021) Ensemble Classification Method for Email Spam Prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract Every day, email users receive a great deal of spam emails from unknown senders in their inboxes. Spamming has indeed been linked to social engineering, which has resulted in online cyber fraud. This usually starts with an email from an untrustworthy source that contains a URL that, when opened, might compromise one's personal data. The concept of machine learning has been well investigated, and there are numerous algorithms that can effectively do this task. Pre-processing, feature engineering, and the machine learning algorithm are the three steps in the pipeline of an email spam filtration system based on machine learning. Some words, such as combination words, articles, and others, are removed from the email composition in the first step of the training filter, which is the pre-processing of e-mails, because they play no part in categorization. Following the receipt of an email, complete this step. Feature engineering is a decision-making method that employs some previously learnt features from a set of training instances. The values of the features may differ. The authors of the email spam detection built the various machine learning methods. In this study, an ensemble machine learning algorithm for email spam prediction will be established.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Security; Machine Learning; Email Spam; Ensemble Classification
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: Tamara Malone
Date Deposited: 05 Jan 2023 16:37
Last Modified: 07 Mar 2023 12:03

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