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REVIEW: "Ending Spam", Jonathan A. Zdziarski

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  • Rob, grandpa of Ryan, Trevor, Devon & Ha
    BKENDSPM.RVW 20051029 Ending Spam , Jonathan A. Zdziarski, 2005, 1-59327-052-6, U$39.95/C$53.95 %A Jonathan A. Zdziarski %C 555 De Haro Street, Suite
    Message 1 of 1 , Jan 19, 2006
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      BKENDSPM.RVW 20051029

      "Ending Spam", Jonathan A. Zdziarski, 2005, 1-59327-052-6,
      %A Jonathan A. Zdziarski
      %C 555 De Haro Street, Suite 250, San Francisco, CA 94107
      %D 2005
      %G 1-59327-052-6
      %I No Starch Press
      %O U$39.95/C$53.95 415-863-9900 fax 415-863-9950 info@...
      %O http://www.amazon.com/exec/obidos/ASIN/1593270526/robsladesinterne
      %O http://www.amazon.ca/exec/obidos/ASIN/1593270526/robsladesin03-20
      %O Audience s+ Tech 3 Writing 2 (see revfaq.htm for explanation)
      %P 287 p.
      %T "Ending Spam"

      The preface states that the book is for those seriously interested in
      spam identification technologies, and concentrates on Bayesian and
      related statistical filtering.

      Part one is an introduction to spam filtering. Chapter one reviews
      the history of spam, although many of the early entries are simply
      annoyances or chain letters rather than the commercial or fraudulent
      items considered under the banner today, and the author does not seem
      to realize that 419 scams predated email by a considerable margin. A
      look at the development of spam filtering (excluding Bayesian) is
      presented in chapter two, along with some non-filtering. Bayesian
      analysis is explained in chapter three, and the statistical filtering
      basis is outlined in chapter four.

      The fundamental actuarial core is expanded in part two. Chapter five
      covers message coding. Tokenization, chunking characters into
      identifiable items, is examined in chapter six. Tricks spammers use
      to evade filters, and the solutions finding spam despite the
      deceptions, are outlined in chapter seven. Storage and performance
      issues raised by the data rules required by statistical filters are
      addressed in chapter eight. Chapter nine looks at aspects of scaling
      to systems supporting large numbers of users.

      Part three deals with advanced concepts in statistical filtering.
      Chapter ten delves into testing which, because of the individual and
      adaptive nature of Bayesian filtering, presents unique challenges.
      Tokenization is revisited in chapter eleven, in more advanced forms.
      Markovian discrimination, with its examination of stateful entities,
      is explained in chapter twelve. Having noted many kinds of features
      in the book, chapter thirteen explores ways to reduce the items used
      (and data required) while maintaining accuracy. Collaborative rule-
      building with other users, groups, or systems is reviewed in chapter

      As the preface implies, this is *not* a book for users who just want
      to install POPFile (although that and other programs are explored in
      an appendix). For those who are seriously involved in managing and
      developing spam filtering, however, the book does provide very useful
      advice, pointers, and research.

      copyright Robert M. Slade, 2005 BKENDSPM.RVW 20051029

      ====================== (quote inserted randomly by Pegasus Mailer)
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      Eat well, stay fit, die anyway
      http://victoria.tc.ca/techrev or http://sun.soci.niu.edu/~rslade
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