Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification

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Overview

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 ...

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Overview

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.

TOCIntroduction

PART I: An Introduction to Spam FilteringChapter 1: The History of SpamChapter 2: Historical Approaches to Fighting SpamChapter 3: Language Classification ConceptsChapter 4: Statistical Filtering Fundamentals

PART II: Fundamentals of Statistical FilteringChapter 5: Decoding: Uncombobulating MessagesChapter 6: Tokenization: The Building Blocks of SpamChapter 7: The Low-Down Dirty Tricks of SpammersChapter 8: Data Storage for a Zillion RecordsChapter 9: Scaling in Large Environments

PART III: Advanced Concepts of Statistical FilteringChapter 10: Testing TheoryChapter 11: Concept Identification: Advanced TokenizationChapter 12: Fifth-Order Markovian DiscriminationChapter 13: Intelligent Feature Set ReductionChapter 14: Collaborative Algorithms

Appendix: Shining Examples of Filtering

Index

Fascinating reading for any geek, this landmark title describes, in-depth, how statistical filtering is being used by next generation spam filters to identify and filter spam.

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Editorial Reviews

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.
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Product Details

  • ISBN-13: 9781593270520
  • Publisher: No Starch Press San Francisco, CA
  • Publication date: 7/28/2005
  • Edition number: 1
  • Pages: 312
  • Product dimensions: 7.00 (w) x 9.24 (h) x 0.72 (d)

Meet the Author

Jonathan Zdziarski is better known as the hacker "NerveGas" in the iOS development community. His work in cracking the iPhone helped lead the effort to port the first open source applications to it, and his book iPhone Open Application Development taught developers how to write applications for the popular device long before Apple introduced its own SDK. Jonathan is also the author of many other books, including iPhone SDK Application Development and iPhone Forensics. Jonathan presently supports over 2,000 law enforcement agencies worldwide and distributes a suite of iOS forensic imaging tools to obtain evidence from iOS devices for criminal cases. He frequently consults and trains law enforcement agencies and assists forensic examiners in their investigations.

Jonathan is also a full-time Sr. Forensic Scientist, where, among other things, he performs penetration testing of iOS applications for corporate clients.

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Table of Contents

1 The history of spam 3
2 Historical approaches to fighting spam 25
3 Language classification concepts 45
4 Statistical filtering fundamentals 63
5 Decoding : uncombobulating messages 87
6 Tokenization : the building blocks of spam 97
7 The low-down dirty tricks of spammers 111
8 Data storage for a zillion records 141
9 Scaling in large environments 157
10 Testing theory 177
11 Concept identification : advanced tokenization 197
12 Fifth-order Markovian discrimination 215
13 Intelligent feature set reduction 227
14 Collaborative algorithms 241
App Shining examples of filtering 257
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  • Anonymous

    Posted November 17, 2005

    Zdziarski a Genuis!

    Zdziarsi is a genius. He has not only written a gread how to, but he does it with style and a welcome bit of humor. Can't wait for the next book!!

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