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Original Essays | September 15, 2014

Lois Leveen: IMG Forsooth Me Not: Shakespeare, Juliet, Her Nurse, and a Novel



There's this writer, William Shakespeare. Perhaps you've heard of him. He wrote this play, Romeo and Juliet. Maybe you've heard of it as well. It's... Continue »
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    Lois Leveen 9781476757445

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

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

 

Synopses & Reviews

Publisher Comments:

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

Synopsis:

Considerable research and some brilliant 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. The author 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. Fascinating reading for any geek.

Synopsis:

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.

About the Author

Jonathan Zdziarski is better known as the hacker "NerveGas" in the iPhone development community. His work in cracking the iPhone helped lead the effort to port the first open source applications, and his book, iPhone Open Application Development, taught developers how to write applications for the popular device long before Apple introduced its own SDK. Prior to the release of iPhone Forensics, Jonathan wrote and supported an iPhone forensics manual distributed exclusively to law enforcement. Jonathan frequently consults law enforcement agencies and assists forensic examiners in their investigations. He teaches an iPhone forensics workshop in his spare time to train forensic examiners and corporate security personnel.

Jonathan is also a full-time research scientist specializing in machine learning technology to combat online fraud and spam, an effort that led him to develop networking products capable of learning how to protect customers. He is founder of the DSPAM project, a high-profile, next-generation spam filter that was acquired in 2006 by Sensory Networks, Inc. He lectures widely on the topic of spam and is a foremost researcher in the fields of machine-learning and algorithmic theory.

Jonathan's website is zdziarski.com.

Product Details

ISBN:
9781593270520
Author:
Zdziarski, Jonathan A.
Publisher:
No Starch Press
Author:
Zdziarski, Jonathan
Subject:
Data Transmission Systems - General
Subject:
Computer programs
Subject:
Electronic mail systems
Subject:
Filters (Mathematics)
Subject:
Spam filtering (Electronic mail)
Subject:
Networking - General
Subject:
Bayesian;Bayesian filtering;DSPAM;Paul Graham;SpamAssassin;UCE;language classification;spam;statistical filtering;tokenization;unsolicited commercial email
Subject:
Bayesian;Bayesian filtering;DSPAM;P
Subject:
aul Graham;SpamAssassin;UCE;language classification;spam;statistical filtering;tokenization;unsolicited commercial email
Subject:
Data Transmission Systems - Electronic Mail
Edition Description:
Print PDF
Publication Date:
20050731
Binding:
TRADE PAPER
Language:
English
Illustrations:
Y
Pages:
312
Dimensions:
9.25 x 7 x 0.72 in 1.12 lb

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Related Subjects

Computers and Internet » Internet » General
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Computers and Internet » Networking » Computer Security
Computers and Internet » Networking » General
Computers and Internet » Personal Computers » Privacy

Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification New Trade Paper
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Product details 312 pages No Starch Press - English 9781593270520 Reviews:
"Synopsis" by , Considerable research and some brilliant 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. The author 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. Fascinating reading for any geek.
"Synopsis" by ,
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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