E-Mail Spam Detection Using Machine Learning

Abstract

The rapid proliferation of digital communication has led to an exponential increase in unsolicited bulk e-mails, commonly known as spam, which poses significant security risks and resource drains. Traditional rule-based filtering methods often fail to keep pace with the evolving tactics of spammers, necessitating more robust, intelligent solutions. This project explores the application of Machine Learning (ML) algorithms to automate the detection and filtration of spam e-mails with high accuracy. By leveraging natural language processing (NLP) techniques, raw email data is preprocessed through tokenization, stop-word removal, and TF-IDF (Term FrequencyInverse Document Frequency) vectorization to extract meaningful features. Several supervised learning models, including Naive Bayes, Support Vector Machines (SVM), and Random Forest, are evaluated based on their ability to classify messages as "ham" (legitimate) or "spam." The performance of these models is measured using key metrics such as precision, recall, and the F1- score to ensure a low rate of false positives, which is critical for user experience. Experimental results demonstrate that probabilistic models like Naive Bayes are particularly effective for text classification due to their computational efficiency and high accuracy. However, ensemble methods often provide superior robustness against sophisticated adversarial attacks. This study concludes that an optimized ML pipeline significantly reduces the manual overhead of e-mail management while enhancing overall cybersecurity. The findings highlight the importance of continuous model retraining to adapt to the dynamic nature of spam content and sender behavior in the modern digital landscape.