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Through considerable research, creative minds have invented 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 spam. Zdziarski explains how spam filtering works and how language classification and machine learning combine to produce remarkably accurate spam filters. 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. Interviews with the creators of many of the best spam filters provide further insight into the anti-spam crusade.
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
Download chapter 11: Sendmail
Editado: Julio 2005
312 páginas
ISBN-10: 1-593270-52-6
ISBN-13: 978-1-59327-052-0
Jonathan A. Zdziarski has been fighting spam for eight years, and has spent a significant portion of the past two years working on the next generation spam filter DSPAM, with up to 99.985% accuracy. Zdziarski lectures widely on the topic of spam.
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