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| <document> |
| |
| <properties> |
| <title>Apache James Server 3 - Antispam Configuration</title> |
| </properties> |
| |
| <body> |
| |
| <section name="Antispam Configuration"> |
| |
| <p>Apache James Server Anti-Spam system can be configured via two main different mechanisms:</p> |
| |
| <ul> |
| <li>SMTP Hooks; see <a href="https://github.com/apache/james-project/tree/master/server/apps/spring-app/src/main/resources/smtpserver.xml">examples</a>.</li> |
| <li>Mailets; see <a href="https://github.com/apache/james-project/tree/master/server/apps/spring-app/src/main/resources/mailetcontainer.xml">examples</a>.</li> |
| </ul> |
| |
| <subsection name="AntiSpam SMTP Hooks"> |
| |
| <p>"FastFail" SMTP Hooks acts to reject before spooling |
| on the SMTP level. SpamAssasin hook can be used as a fastfail hook, therefore |
| SpamAssassin must run as a server on the same machine as the Apache James Server.</p> |
| |
| <p>SMTP Hooks for non-existent users, DSN filter, domains with invalid MX record, |
| can also be configured.</p> |
| |
| </subsection> |
| |
| <subsection name="AntiSpam Mailets"> |
| |
| <ul> |
| <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 users’ judgment. |
| Users need to manually judge as spam and send to spam@thisdomain.com, oppositely, |
| if not spam they then send to not.spam@thisdomain.com. 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> |
| |
| </subsection> |
| |
| </section> |
| |
| </body> |
| |
| </document> |
