SHRED: An Ensemble-Based Machine Learning Model to Sift Email Messages for Real-Time Spam Detection

This project aims to develop a robust and efficient system that can classify emails as spam or not based on their content and metadata. The rapid increase in the volume of email communication has made it essential for email service providers to integrate spam detection systems that can automatically filter unwanted messages. Traditional methods of spam detection, such as rule-based filtering, have become ineffective due to the complexity and diversity of spam messages. To address this, SHRED utilizes an ensemble learning approach, combining multiple models like Convolutional Neural Networks (CNN), XGBoost, HybridBoost, and StreamSHRED – Online Incremental Ensemble. The ensemble method is designed to enhance the system's accuracy by integrating the strengths of various algorithms, enabling it to effectively differentiate between spam and non-spam messages. The system also incorporates an online incremental learning mechanism via StreamSHRED, which allows the model to continuously learn from new data without requiring retraining on the entire dataset. This is particularly useful in an evolving environment where spam tactics change rapidly. The dataset used for training consists of email metadata, such as sender, recipient, and subject, as well as the content of the message. The system preprocesses this data, extracts relevant features, and then applies machine learning models for classification. The overall objective is to develop a system that ensures high accuracy, reduced false positives, and enhanced user satisfaction. The web-based interface allows users to input email messages and receive real-time predictions, making it a user-friendly tool for email services. The system is trained to predict the likelihood of an email being spam based on its content, which can be further used to block or categorize emails accordingly.

Keywords: Email Classification, Spam Detection, Ensemble Learning, XGBoost, CNN, HybridBoost, StreamSHRED, Online Incremental Learning, Feature Extraction, Real-time Prediction.