bt-MailFilter FAQ’s
bt-MailFilter must be installed on a Microsoft Exchange Server 2000 or 2003.
However for remote management, the MailFilter GUI may be installed on any Windows 2000 or XP machine.
Can I block attachments?
Yes, MailFilter can block attachments based on the file type and/or file size, both incoming and outgoing. Based on user-defined rules, the attachment may be deleted and the email message delivered or the email message may deleted, quarantined, etc.
Can I prevent proprietary information from being sent to competitors?
Yes, MailFilter allows you to block outgoing messages using keyword or phrases and attachment restrictions..
How easy is it to install MailFilter and implement email filter rules?
MailFilter must be installed on the MS Exchange server. After installation, the administrator inputs the local domain (i.e. @mycompany.com). Immediately, MailFilter will filter incoming mail using a 3rd party blacklist, known virus attachments and categorized keywords and phrases. Other sample inbound and outbound rules are installed but not implemented. To implement these, simply check the 'enable' box for the appropriate rule.
Does MailFilter support remote administration?
MailFilter has a 'remote client' capability allowing you to manage all functions from your workstation.
I have multiple exchange servers, does MailFilter support this environment?
Yes, MailFilter can 'share' rules, keywords etc across the network.
What exactly is Bayesian Filtering ?
Bayesian filters are the most efficient and are auto-adaptive and eliminate false positives almost completely. Bayesian spam filters calculate the probability of a message being spam based on its contents. They learn from spam and from valid or good email, resulting in a very robust and efficient anti-spam approach that rarely returns false positives.
Bayesian filters do away with the problems of simple scoring or keyword spam filters, and it does so radically. Since the weakness of scoring filters is in the manually built list of characteristics and their scores, this list is eliminated. Instead, Bayesian filters build the list themselves. Start with a large number of emails that you have classified as spam, and another batch of valid email. The filter looks at both, and analyzes the legitimate mail as well as the spam to calculate the probability of various characteristics appearing in spam, and in good email.
The characteristics a Bayesian spam filter can look at can be the words in the body of the message and its headers (senders and message paths), but also other aspects such as HTML code (like colors), or word pairs, phrases and meta information (where a particular phrase appears).
When a new message arrives, it is analyzed by the Bayesian filter, and the probability of the complete message being spam is calculated using the individual characteristics. From these words alone it's not yet clear whether it is spam or valid email. But other characteristics will indicate a probability that allows the filter to classify the message as either spam or valid email.
Bayesian filters automatically adopt. The above message can be used to further train the filter. In this example, either the probability of "a good or valid word" indicating valid email is lowered, or the probability of "a bad or invalid word" indicating spam must be reconsidered. Using this auto-adaptive technique, Bayesian filters can learn from both their own and the user's decisions.