Description
This landmark title describes, in-depth, how statistical filtering is being used by next generation spam filters to identify and filter spam. Readers gain a complete understanding of the mathematical approaches used in today's spam filters, decoding, tokenization, the use of various algorithms (including Bayesian analysis and Markovian discrimination), and the benefits of using open-source solutions to end spam.
Full Description
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: Data Storage 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
