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.
Anti-Spam Technologies
Spam Problem
Prevalent Anti-Spam Technologies
Email Feature Extraction Approaches
Email Classification Techniques
Performance Evaluation and Standard Corpora
Summary
Artificial Immune System
Introduction
Biological Immune System
Artificial Immune System
Applications of AIS in Anti-Spam
Summary
Term Space Partition-Based Feature Construction Approach
Motivation
Principles of the TSP Approach
Implementation of the TSP Approach
Experiments
Summary
Immune Concentration-Based Feature Construction Approach
Introduction
Diversity of Detector Representation in AIS
Motivation of Concentration-Based Feature
Overview of Concentration-Based Feature
Gene Library Generation
Concentration Vector Construction
Relation to Other Methods
Complexity Analysis
Experimental Validation
Discussion
Summary
Local Concentration-Based Feature Extraction Approach
Introduction
Structure of Local Concentration Model
Term Selection and Detector Sets Generation
Construction of Local Concentration-Based Feature Vectors
Strategies for Defining Local Areas
Analysis of Local Concentration Model
Experimental Validation
Summary
Multi-Resolution Concentration-Based Feature Construction Approach
Introduction
Structure of Multi-Resolution Concentration Model
Multi-Resolution Concentration-Based Feature Construction Approach
Weighted Multi-Resolution Concentration-Based Feature Construction Approach
Experimental Validation
Summary
Adaptive Concentration Selection Model
Overview of Adaptive Concentration Selection Model
Setup of Gene Libraries
Construction of Feature Vectors Based on Immune Concentration
Implementation of Adaptive Concentration Selection Model
Experimental Validation
Summary
Variable Length Concentration-Based Feature Construction Method
Introduction
Structure of Variable Length Concentration Model
Experimental Parameters and Setup
Experimental Results on the VLC Approach
Discussion
Summary
Parameter Optimization of Concentration-Based Feature Construction Approaches
Introduction
Local Concentration-Based Feature Extraction Approach
Fireworks Algorithm
Parameter Optimization of Local Concentration Model for Spam Detection by Using Fireworks Algorithm
Experimental Validation
Summary
Immune Danger Theory-Based Ensemble Method
Introduction
Generating Signals
Classification Using Signals
Self-Trigger Process
Framework of DTE Model
Analysis of DTE Model
Filter Spam Using the DTE Model
Summary
Immune Danger Zone Principle-Based Dynamic Learning Method
Introduction
Global Learning and Local Learning
Necessity of Building Hybrid Models
Multi-Objective Learning Principles
Strategies for Combining Global Learning and Local Learning
Local Trade-Off between Capacity and Locality
Hybrid Model for Combining Models with Varied Locality
Relation to Multiple Classifier Combination
Validation of the Dynamic Learning Method
Summary
Immune-Based Dynamic Updating Algorithm
Introduction
Backgrounds of SVM and AIS
Principles of EM-Update and Sliding Window
Implementation of Algorithms
Filtering Spam Using the Dynamic Updating Algorithms
Discussion
Summary
AIS-Based Spam Filtering System and Implementation
Introduction
Framework of AIS-Based Spam Filtering Model
Postfix-Based Implementation
User Interests-Based Parameter Design
User Interaction
Test and Analysis
Summary
Biography
Ying Tan, PhD, is a full professor and PhD advisor in the School of Electronics Engineering and Computer Science at Peking University, China. He is also director of the Computational Intelligence Laboratory at Peking University. He received his PhD from Southeast University in Nanjing, China. His research interests include computational intelligence, swarm intelligence, data mining, machine learning, fireworks algorithm, and intelligent information processing for information security. He has published more than 280 papers, has authored or coauthored six books and more than 10 book chapters, and holds three invention patents. He is editor in chief of the International Journal of Computational Intelligence and Pattern Recognition and is an associate editor of IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning Systems. He is the general chair of the ICSI–CCI 2015 joint conference and ICSI series conference and is a senior member of the IEEE.
