International Journal of Science, Engineering and TechnologyAuthors: Sumit Ghimire, Bhoj Raj Ghimire
Abstract: Email communication plays a critical role in modern organizations. The increasing volume of spam, phishing, and malicious emails poses significant security and productivity challenges. This study investigates the effectiveness of machine learning techniques for organizational email spam detection using machine learning-based classification approaches. Enron Email Dataset and a labeled spam collection dataset containing 5,572 messages categorized as ham and spam. The data were preprocessed through text normalization, tokenization, stop-word removal, and feature extraction using sequence encoding and padding techniques. Four deep learning architectures Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Temporal Convolutional Network (TCN) were implemented and optimized. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, training time, CPU utilization, and memory consumption. Experimental results showed that all models achieved high classification performance, with accuracy exceeding 97%. The findings indicate that deep learning approaches provide effective and practical solutions for organizational email spam detection by balancing classification accuracy and computational efficiency.
DOI: http://doi.org/10.5281/zenodo.20811672
