Enhancing Email Security: A Real-Time Machine Learning-Based Spam Detection System

Abstract

Spam emails pose a persistent threat to online security and productivity. To combat this menace, we propose a robust machine learning-based spam email detection system, leveraging algorithms like Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF). Our system integrates Python's Natural Language Processing (NLP) library and cloud sourcing to achieve unparalleled accuracy and adaptability. Advanced feature extraction techniques, including Term Frequency-Inverse Document Frequency (TF-IDF) and N-grams, enable comprehensive analysis of email content. Real-time scanning and detection capabilities ensure immediate protection, while continuous learning allows the system to evolve and counter emerging spam tactics. Our system demonstrates exceptional performance, achieving accuracy rates of up to 97.58% with SVM. Comparative analysis of 11 machine learning algorithms reveals notable performance variations. By minimizing false positives and negatives, our system provides reliable protection against unwanted and harmful emails. This research contributes to the development of effective spam detection solutions, offering insights into optimal algorithms, time complexity-accuracy trade-offs, and scalable system design. Our system has significant implications for enhancing email security, safeguarding users from security breaches, and mitigating the economic impacts of spam.