Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification by Jonathan A. Zdziarski

From the Publisher

Join author John Zdziarski for a look inside the brilliant minds that have conceived clever new ways to fight spam in all its nefarious forms. This landmark title describes, in-depth, how statistical filtering is being used by next-generation spam filters to identify and filter unwanted messages, how spam filtering works and how language classification and machine learning combine to produce remarkably accurate spam filters.

After reading "Ending Spam," you'll have a complete understanding of the mathematical approaches used by today's spam filters as well as decoding, tokenization, various algorithms (including Bayesian analysis and Markovian discrimination) and the benefits of using open-source solutions to end spam. Zdziarski interviewed creators of many of the best spam filters and has included their insights in this revealing examination of the anti-spam crusade.

If you're a programmer designing a new spam filter, a network admin implementing a spam-filtering solution, or just someone who's curious about how spam filters work and the tactics spammers use to evade them, "Ending Spam" will serve as an informative analysis of the war against spammers.

TOC

Introduction

PART I: An Introduction to Spam Filtering

Chapter 1: The History of Spam

Chapter 2: Historical Approaches to Fighting Spam

Chapter 3: Language Classification Concepts

Chapter 4: Statistical Filtering Fundamentals

PART II: Fundamentals of Statistical Filtering

Chapter 5: Decoding: Uncombobulating Messages

Chapter 6: Tokenization: The Building Blocks of Spam

Chapter 7: The Low-Down Dirty Tricks of Spammers

Chapter 8: DataStorage for a Zillion Records

Chapter 9: Scaling in Large Environments

PART III: Advanced Concepts of Statistical Filtering

Chapter 10: Testing Theory

Chapter 11: Concept Identification: Advanced Tokenization

Chapter 12: Fifth-Order Markovian Discrimination

Chapter 13: Intelligent Feature Set Reduction

Chapter 14: Collaborative Algorithms

Appendix: Shining Examples of Filtering

Index

Slashdot.org

Jonathan Zdziarski has made another significant contribution (after DSPAM) to the anti-spam community. Whether you are a system administrator, anti-spam researcher, engineer or a newbie interested in fighting spam, this book is a great reference.

Product Details

• Table of Contents

Table of Contents