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.