POP3Filter Features:

• Advanced bayesian style automatic learning

  POP3Filter makes use of an advanced bayesian classifier as its primary method of classifying messages as spam.

  Bayesian classifiers in general are recognised as one of the best methods for catching spam as they use statistical methods to learn from the types of both spam and non spam email that you receive. Because of their learning nature, they automatically adapt to the ever changing face of spam, rather than needing regular updates like ordinary rule based spam filters require. Also, because of their personalized nature, spammers are unable to test that their spam will get past bayesian filters before they send it out.

  Enhancements to the bayesian classifier found within POP3Filter include automatic learning from incoming messages and the ability to discriminate likely spam words even when spammers try to hide or obscure them - e.g. by spelling "viagra" as "v1@gra"

  The bayesian classifier in POP3Filter alone accounts for around 98-99% of all spam messages being caught.

• Automatic whitelisting of known senders

  Sometimes people you know send you emails that would otherwise appear to be spam to the bayesian classifier - e.g. spammers often send their message as an image to try to hide their spam words, so an image on its own is normally a good indicator that a message is spam - people you don't know are unlikely to just send you a picture without an explanation.

  The automatic whitelisting feature of POP3Filter helps prevent false positives, even when people you know send you messages that otherwise look like spam.

• Uses URI blacklists to improve accuracy

  Most spam advertises a web site where the spammer tries to entice you to spend money, etc. This provides a weak point that can be used to detect a reasonable proportion of spam.

  Because URI blacklists are reactive rather than proactive, and not all spam messages actually contain a web site address, URI blacklisting at best can block only about 80% of spam.

  POP3Filter utilizes URI blacklists to assist in cases where the bayesian classifier isn't sure whether an email is spam. It also helps detect some spam which would otherwise be classified as not spam by the bayesian classifier.

• Automatic header analysis

  Although not entirely reliable on its own, analysing the headers of incoming emails helps POP3Filter detect spam when the bayesian classifier is not entirely certain on the classification to give to an email.

• Detects and blocks viruses

  POP3Filter includes code specifically to detect and block virtually all viruses, without the need for regularly updated signatures.

• Compatible with all POP3 compliant mail clients running on Windows 95/98/Me/NT/2000/XP/2003

  POP3Filter runs on all 32-bit versions of Windows from 95 through to 2003. It supports all POP3 compatible mail clients and servers.

• Automatic database cleanup

  Spammers regularly include random and junk words in their spam in an attempt to reduce the effectiveness of spam filters.

  POP3Filter automatically cleans its database on a regular basis to maintain accuracy and speed and to also keep the database size small.