Improving Spam Detection in Email with Transfer Learning and Deep Learning

Detecting spam emails is still a major cybersecurity challenge that impacts both individuals and organizations. This problem has been tackled using both deep learning and traditional machine learning techniques, with BERT-based models demonstrating positive results. However, the efficacy of current models is limited because they frequently miss contextual details and long-range dependencies in email text. To enhance the precision of spam classification, we present a novel ensemble model that combines BERT with an attention mechanism. The attention mechanism improves contextual understanding and decision-making by helping the model concentrate on the most pertinent words and phrases. Comprehensive tests on benchmark datasets show that our approach achieves superior performance compared to deep learning models like LSTM and BERT, as well as conventional machine learning classifiers like Naive Bayes and SVM. In order to demonstrate how our model improves interpretability and robustness against adversarial samples, we also examine feature importance and attention visualization. According to the results, our ensemble model is a practical choice for email service providers and enterprises since it is scalable and efficient for real-world spam detection. Our suggested model outperforms the baseline model used in earlier studies, achieving an accuracy of 99.31%. Enhancing real-time processing capabilities and multilingual spam detection will be the main goals of future work.