Spam Classification Using Machine Learning and Deep Learning

No Thumbnail Available
Authors
Ahmad, Sheikh Muhammad
Issue Date
2024
Degree
MSc Cybersecurity
Publisher
Dublin Business School
Rights
Abstract
This 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.