Machine Learning-Based Spam Email Filter: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system: Identify and filter spam emails using machine learning algorithms to enhance email security.
Scope of the system: Applicable for personal and organizational email systems.
2. Requirements Analysis
Functional Requirements:
· - Analyze email content for spam indicators.
· - Classify emails as spam or legitimate.
· - Provide feedback mechanisms to improve accuracy over time.
Non-Functional Requirements:
· - High classification accuracy and low false positives.
· - Scalability to process large volumes of emails.
· - Low latency to ensure real-time filtering.
3. System Design
Architecture:
· - Centralized system with modules for feature extraction, model training, and prediction.
· - Integration with email servers for seamless operation.
Data Flow Diagrams (DFDs):
· - Level 0: Overview of email data flow through the system.
· - Level 1: Modules like Preprocessing, Feature Extraction, and Classification.
Database Design:
· - Tables: Email Logs, Feature Vectors, Classification Results.
4. Technology Stack
Programming Languages:
· - Python for machine learning and backend development.
Machine Learning Libraries:
· - Scikit-learn, TensorFlow, or PyTorch for building and training models.
Email Integration:
· - IMAP or SMTP protocols for accessing and processing emails.
Database:
· - MySQL, PostgreSQL, or MongoDB for storing email data and classification results.
Frontend (optional):
· - Web-based dashboard using Flask or Django for visualization.
5. Implementation
Data Collection:
· - Gather datasets like the Enron Email Dataset or spam email archives for training.
Feature Extraction:
· - Extract features like word frequency, presence of URLs, and email metadata.
Model Development:
· - Train models using algorithms like Naive Bayes, SVM, or Neural Networks.
· - Evaluate models using metrics like accuracy, precision, recall, and F1-score.
Integration:
· - Connect the model with email servers for real-time filtering.
· - Implement APIs for feedback and retraining.
6. Security
Encrypt email data during storage and transmission.
Ensure compliance with data privacy regulations like GDPR.
Regularly audit and update the system to address vulnerabilities.
7. Testing
Unit Testing: Validate individual components like feature extraction and model prediction.
Integration Testing: Ensure seamless interaction between the model and email servers.
System Testing: Test the entire system under real-world email traffic conditions.
Performance Testing: Evaluate model accuracy and system response time.
8. Deployment
Deploy the trained model on cloud or on-premise servers.
Integrate with email services for live filtering.
Monitor system performance and refine models as needed.
9. Maintenance and Updates
Regularly retrain models with new data to maintain accuracy.
Update system components for improved functionality and security.
Monitor system logs and user feedback for continuous improvement.
10. Appendix
Glossary of terms.
References and additional resources.