What filtering mechanisms does MailWasher Server use?
Content Analysis:
Statistical Content Analysis Filtering. Statistical Content Filtering based on Bayesian, lexical analysis and trait analysis can be applied to incoming email to accurately identify and remove spam, while reducing false positive occurrences to a minimum. Statistical Content Filtering can be set to one of five levels of sensitivity: Most Conservative, Conservative, Moderate, Most Aggressive, or Aggressive. Statistical content analysis filtering works by using lexical, trait and statistical analysis to determine the overall probability that a message is spam by learning what an organization (and each individual) identifies as spam. It also involves checking for traits in the header of each message to weed out messages that are most likely spam.
Connection Filtering:
Real-time Blackhole List Servers. MailWasher Server checks incoming SMTP servers’ IP address against Real-time Blackhole Lists (RBL’s) so that only non-blacklisted servers are allowed to send messages to the server.
Blacklists and Whitelists. IT administrators and users have the ability to set and control blacklists and whitelists, through the MailWasher online web control panel. Email addresses of legitimate senders added to the white list will automatically bypass the antispam filters.
Message Signatures:
FirstAlert! Global Spam Database. Adding to MailWasher Server’s comprehensive, multi-layered approach, MailWasher Server uses the FirstAlert! global spam database – a 24/7 operation which makes use of a global network of users reporting unsolicited email which is then verified and categorized by our dedicated FirstAlert! team. Users are able to submit unsolicited email to FirstAlert! which is updated in real time.
Can you proivide a summary of each filtering mechanism?
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The following table provides a summary of MailWasher Server's filtering features.
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Filter
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Description
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Whitelist
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The whitelist includes email addresses from which all emails are accepted, regardless of their content. None of MailWasher Server's junk mail filters are applied to messages from addresses on the whitelist, therefore care must be taken when adding addresses. It is possible to avoid false negatives by ensuring that you do not add entire domain names to your whitelist, for example, *@aol.com.
For more information about how to use the whitelist, see the MailWasher Server Help file.
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Blacklist
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MailWasher Server filters all messages from addresses that appear on the Address blacklist. All users are affected by the Address blacklist, therefore it is recommended that entire domains are not added to the blacklist as this prevents all end users from receiving possible legitimate messages from any address at that domain.
For more information about how to use the Address blacklist, see the MailWasher Server Help file.
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IP-based RBLs
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Real-time blackhole lists (RBLs) are used to list the servers and domains of organisations that have been identified as senders of junk emails. IP-based RBLs (ip4r RBLs) are lists of IP addresses of servers that have been identified as sending or relaying junk mail. Firetrust recommends that you carefully investigate each RBL service for accuracy, before you begin using them. Inaccurate RBLs can result in a high false positive rate.
For more information about how to use RBLs, see the MailWasher Server Help file.
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Domain-based RBLs
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Domain-based real-time blackhole lists (rhsbl RBLs) are lists of domain names from the return-path address on received junk email messages. False positives are possible if an RBL lists a domain that spammers have been spoofing messages from. Spammers often spoof the email address that they send from, which can result in these domains being erroneously added to a domain-based RBLs blacklist.
For more information about how to use RBLs, see the MailWasher Server Help file.
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FirstAlert!
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FirstAlert! is a database of reported and known junk mail messages that is used to eliminate future circulation of junk mail. Both FirstAlert! users and administrators verify the junk mail, therefore ensuring a high rate of accuracy before messages are added to the database.
For more information about how to use FirstAlert!, see the MailWasher Server Help file.
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Statistical content analysis
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MailWasher Server's statistical content analysis is similar to Bayesian filtering, but has the addition of using advanced trait based analysis as well as traditional word based analysis. Junk messages that match statistical content filters are always rejected without notification by MailWasher Server, however these messages display as quarantined. False positives may occur during the initial phase of training the filter, but this can be limited by modifying the sensitivity of the filter.
For more information about how to use statistical content analysis filters, see the MailWasher Server Help file.
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In what order are messages filtered?
The diagram below illustrates the MailWasher Server message filtering process.
