"ending Spam" Answers Critical Questions For Those Who Want To Do More Than Just Delete Unwanted Messages
Tuesday, 21 June 2005 16:11 EST
June 21, 2005, San Francisco--No Starch Press, publishers of the finest in geek entertainment, has announced the release of "Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification" (No Starch Press, July '05) by Jonathan Zdziarski. This important new title describes, in-depth, how next-generation spam filters work to identify and dispose of spam. It cracks open the "black box" to reveal the guts of spam filtering techniques and will be a fascinating read for those writing next generation spam filters, sys admins who need to implement them, gearheads who need to satisfy their desire to know how things work, and, of course, spammers who want insights into beating the filters.
"While other titles have explored the spam business and the personalities involved, there hasn't been a book examining the technology being used on the front lines to fight spam--until now," said William Pollock, founder and president of No Starch Press. "Lots of computer professionals and enthusiasts alike want and need to understand spam on a deeper level."
Just as with spyware and viruses, there is a constant tug-of-war between the spammers and those who seek to banish them. For each sneaky spammer thwarted, there are countless others devising new and unbeaten ways of penetrating your inbox. Fortunately, the open source community and many of its seasoned hackers have developed new solutions using approaches rooted in traditional mathematics. "Ending Spam" explains exactly how those spam filtering techniques work.
Author Zdziarski maintains DSPAM, a high-profile spam filter hailed on Slashdot and Wired News that is able to achieve levels of accuracy up to
99.985 percent. In "Ending Spam," he explains how spam filtering works and how language classification and machine learning combine to produce remarkably accurate spam filters. Readers will 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 (SpamProbe, Bogofilter, Death2Spam) provide further insight into the anti-spam crusade.
Other topics covered include the many low-down dirty tricks spammers use to push their spam into your inbox, the history of spam, the current state of the market and a detailed examination of some of the most popular open source spam filters and how to install/use them.
ABOUT THE AUTHOR:
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. His research in algorithmic theory and neural networking has led to the development of many new approaches in language classification, and he has played a key role in designing some popular algorithms in use today, including Message Inoculation, Bayesian Noise Reduction, and the first functional Neural Networking algorithm for spam filters. Zdziarski lectures widely on the topic of spam and was a speaker at the 2004 and 2005 MIT Spam Conference.
Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification by Jonathan A. Zdziarski July 2005, 312 pp., $39.95, ISBN 6 Available at fine bookstores everywhere, from www.oreilly.com/nostarch, or directly from No Starch Press (www.nostarch.com, , ).
No Starch Press titles are distributed throughout the U.S. by O'Reilly Media.
ABOUT NO STARCH PRESS: Since 1994, No Starch Press has published the finest in geek entertainment--unique books on technology, with a focus on Open Source, security, hacking, programming, and alternative operating systems. Our titles have personality, our authors are passionate, and our books tackle topics that people care about. More information is available at www.nostarch.com.
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