Spam Classification Using Machine Learning and Deep Learning

dc.contributor.authorAhmad, Sheikh Muhammad
dc.date.accessioned2024-03-28T14:22:44Z
dc.date.available2024-03-28T14:22:44Z
dc.date.issued2024
dc.description.abstractThis research explores a comprehensive approach to refining spam classification accuracy by integrating traditional machine learning and advanced deep learning models. Our evaluation encompasses four diverse models—Adaboost, XGBoost, Long Short-Term Memory (LSTM), Feedforward Neural Network (FFN), and an innovative Transformer-CNN hybrid model. Notably, XGBoost emerges as the frontrunner, achieving a remarkable accuracy of 97.84%, closely trailed by Adaboost at 96.72%. The deep learning counterparts, LSTM and FFN, demonstrate competitive accuracies of 96.47% and 97.67%, respectively. Furthermore, the proposed Transformer-CNN hybrid model exhibits a commendable accuracy of 97.07%. This study underscores the pivotal role of amalgamating diverse machine learning paradigms, emphasizing the efficacy of hybrid models in significantly enhancing accuracy and overall performance in spam classification. The findings contribute valuable insights to the domain, showcasing the potential of a unified approach in fortifying email security and advancing the state-of-the-art in spam detection mechanisms.
dc.identifier.citationAhmad, S.M. (2024). Spam Classification Using Machine Learning and Deep Learning. Masters Thesis, Dublin Business School.
dc.identifier.urihttps://hdl.handle.net/10788/4500
dc.language.isoen
dc.publisherDublin Business School
dc.rights.holderCopyright: The author
dc.rights.urihttp://esource.dbs.ie/copyright
dc.subjectSpam filtering (Electronic mail)
dc.subjectDeep learning
dc.subjectMachine learning
dc.titleSpam Classification Using Machine Learning and Deep Learning
dc.typeThesis
dc.type.degreelevelMSc
dc.type.degreenameMSc Cybersecurity
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