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Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classificationby Jonathan Zdziarski
Synopses & Reviews
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.
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
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.
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.
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Computers and Internet » Internet » General