Anti-Spam Techniques Based on Artificial Immune System

This book introduces anti-spam techniques based on the artificial immune system (AIS). It discusses key topics in AIS-based anti-spam techniques, including extraction methods, construction approaches, classifiers based on immune danger theory, and the immune-based dynamic algorithm plus strategies for designing and applying spam-filtering models.

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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.