The history of Spam Historical approaches to fighting Spam Language classification concepts Statistical filtering fundamentals Decoding Tokenization Tricks of Spammers Data storage for a zillion records Scaling in large environments Testing theory Concept identification Fifth order Markovian discrimination Intelligent feature set reduction Collaborative algorithms
Join author Jonathan 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 curios 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.