Anti-Spam Techniques Based on Artificial Immune System: 1st Edition (Hardback) book cover

Anti-Spam Techniques Based on Artificial Immune System

1st Edition

By Ying Tan

CRC Press

236 pages | 104 B/W Illus.

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Description

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

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

About the Author

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.

Subject Categories

BISAC Subject Codes/Headings:
COM037000
COMPUTERS / Machine Theory
COM051240
COMPUTERS / Software Development & Engineering / Systems Analysis & Design
COM053000
COMPUTERS / Security / General