Author: eric
Date: Tue May 17 11:51:12 2011
New Revision: 1104171
URL: http://svn.apache.org/viewvc?rev=1104171&view=rev
Log:
Draft and uncomplete documentation for antispam configuration in server
(JAMES-1219)
Modified:
james/server/trunk/src/site/xdoc/config-antispam.xml
Modified: james/server/trunk/src/site/xdoc/config-antispam.xml
URL:
http://svn.apache.org/viewvc/james/server/trunk/src/site/xdoc/config-antispam.xml?rev=1104171&r1=1104170&r2=1104171&view=diff
==============================================================================
--- james/server/trunk/src/site/xdoc/config-antispam.xml (original)
+++ james/server/trunk/src/site/xdoc/config-antispam.xml Tue May 17 11:51:12
2011
@@ -27,6 +27,32 @@
<section name="Antispam Configuration">
+ <p>Apache James Server Anti-Spam system can be configured via
+ SMTP Hook (see <a
href="https://svn.apache.org/repos/asf/james/server/trunk/container-spring/src/main/config/examples/smtpserver.xml";>examples</a>).
+ and Mailet (see <a
href="https://svn.apache.org/repos/asf/james/server/trunk/container-spring/src/main/config/examples/mailetcontainer.xml";>examples</a>)
+ </p>
+ <ul>
+ <li>FastFail SMTP Hooks :This first part acts to reject before spooling
+ on the SMTP level. SpamAssasin hook can be used.
+ Non-existent users, DSN filter, domains with invalid MX record,
+ can also be rejected.</li>
+ <li>Black listing Mailet</li>
+ <li>Grey listing Mailet</li>
+ <li>SpamAssassin Mailet is designed to classify the messages as spam or
not
+ with an configurable score threshold. Usually a message will only be
+ considered as spam if it matches multiple criteria; matching just a
single test
+ will not usually be enough to reach the threshold.</li>
+ <li>BayesianAnalysis in the Mailet uses Bayesian probability to classify
mail as
+ spam or not spam. It relies on the training data coming from the
usersâ judgment.
+ Users need to manually judge as spam and send to
[email protected], oppositely,
+ if not spam they then send to [email protected].
BayesianAnalysisfeeder learns
+ from this training dataset, and build predictive models based on
Bayesian probability.
+ There will be a certain table for maintaining the frequency of
Corpus for keywords
+ in the database. Every 10 mins a thread in the BayesianAnalysis will
check and update
+ the table. Also, the correct approach is to send the original spam
or non-spam
+ as an attachment to another message sent to the feeder in order to
avoid bias from the
+ current sender's email header.</li>
+ </ul>
</section>
</body>
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