REVIEW: "Ending Spam", Jonathan A. Zdziarski

Discussion in 'Computer Security' started by Rob Slade, doting grandpa of Ryan and Trevor, Jan 19, 2006.

  1. 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
    %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


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    Rob Slade, doting grandpa of Ryan and Trevor, Jan 19, 2006
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