Anti-Spam Techniques Based on Artificial Immune System

Email has become an indispensable communication tool in daily life. However, high volumes of spam waste resources, interfere with productivity, and present severe threats to computer system security and personal privacy. This book introduces research on anti-spam techniques based on the artificial immune system (AIS) to identify and filter spam. It provides a single source of all anti-spam models and algorithms based on the AIS that have been proposed by the author for the past decade in various journals and conferences.

Inspired by the biological immune system, the AIS is an adaptive system based on theoretical immunology and observed immune functions, principles, and models for problem solving. Among the variety of anti-spam techniques, the AIS has been highly effective and is becoming one of the most important methods to filter spam. The book also focuses on several key topics related to the AIS, including:

• Extraction methods inspired by various immune principles
• Construction approaches based on several concentration methods and models
• Classifiers based on immune danger theory
• The immune-based dynamic updating algorithm
• Implementing AIS-based spam filtering systems

The book also includes several experiments and comparisons with state-of-the-art anti-spam techniques to illustrate the excellent performance AIS-based anti-spam techniques.

Anti-Spam Techniques Based on Artificial Immune System

gives practitioners, researchers, and academics a centralized source of detailed information on efficient models and algorithms of AIS-based anti-spam techniques. It also contains the most current information on the general achievements of anti-spam research and approaches, outlining strategies for designing and applying spam-filtering models.

Table of Contents

Chapter 1: Anti-Spam Technologies
Chapter 2: Artificial Immune System
Chapter 3: Term Space Partition-Based Feature Construction Approach
Chapter 4: Immune Concentration-Based Feature Construction Approach
Chapter 5: Local Concentration-Based Feature Extraction Approach
Chapter 6: Multi-Resolution Concentration-Based Feature Construction Approach
Chapter 7: Adaptive Concentration Selection Model
Chapter 8: Variable Length Concentration-Based Feature Construction Method
Chapter 9: Parameter Optimization of Concentration-Based Feature Construction Approaches
Chapter 10: Immune Danger Theory-Based Ensemble Method
Chapter 11: Immune Danger Zone Principle-Based Dynamic Learning Method
Chapter 12: Immune-Based Dynamic Updating Algorithm
Chapter 13: AIS-Based Spam Filtering System and Implementation