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Various techniques were developed and tried. But spammers quickly make them obsolete using even newer tricks. Fortunately, Reverend Tom Bayes, who lived in the 18th century, has given us a powerful weapon against spam. He wrote a formula for guessing how likely it is that something will happen based on two or more independent events. Several years ago Paul Graham proposed to use this formula to guess whether a piece of e-mail contains spam. The new method is called "Bayesian filtering". Its main difference is its ability to evolve with time. Spammers develop new tricks to fool spam-protection systems, but Bayesian filters learn to catch these tricks immediately.
Spam Reader is an MS Outlook plugin that extends this powerful e-mail software's functionality with a Bayesian spam filter. There is no need to run an external program. Spam Reader fully integrates into MS Outlook. After installing it, you will see a new toolbar and a new item in the main Outlook menu.
Spam Reader analyzes each message when you receive it and puts it into the Spam folder as needed. Spam Reader does not break your existing Outlook filtering rules. This allows you to safely use it with multiple mail list folders and usenet accounts. Spam Reader ships with a large database of known junk e-mail fingerprints. It is trained on a database of more than 20,000 spam messages. Updates are provided from the Spam Reader web site on a regular basis.
In addition to using the pre-installed spam database, you can create your own spam protection rules. This doesn't require any special knowledge. Just point at a message and tell Spam Reader whether it should treat mail like that as spam or not. You can also add mail senders into the White and the Black lists.
Don't waste your time reading junk mail. Thanks to Reverend Tom Bayes and Spam Reader, we now have a cure against spam.
Spam Reader key features:
Supporting all types of mailboxes - Spam Reader can be used with all mail accounts supported by Outlook: POP3, IMAP, HTTP and Exchange.
Bayesian Spam Filter - Implemented spam blocker is based on a Bayesian algorithm that determines the probability for a particular word or phrase to be in a spam message. This probability is used to decide whether a whole message is spam or not. This approach to filtering mail is one of the most natural and thus precise and reliable. The Bayesian algorithm uses the database, which is the result of statistical analysis of more than 20,000 spam messages.
Self-Training - Apart from regular web-updates Spam Reader increases filtering accuracy by analyzing a user's personal mailbox. The results of the analysis are used to correct the database used for spam detection. This technology makes anti-spam protection more reliable and accurate for a particular user and provides most effective protection against spammers who permanently changes tactics for spam attacks.
White List - To make filtering safer, Spam Reader uses a technique called "White List". This technique guarantees that the messages from user's regular correspondents will not be blocked as spam even if their contents look like spam. White List may include names, addresses or entire domains. The list is automatically created on the first program execution by scanning a user's address book and all saved sent messages. Also, Spam Reader automatically updates White List adding the information about the recipients of outgoing messages.
Coordinated interaction with Outlook Rules - Spam Reader features a special algorithm for interaction with Microsoft Outlook Rules. This algorithm allows the user to set the order of executing Outlook Rules and Spam Reader. This option prevents chaotic movement of spam messages between Spam folder and destination folder for some Outlook Rule in case the spam messages match up with this Rule.
Mailing lists detection - Spam Reader supports automatic recognition of newsletters and different mailing lists in order to prevent accidental filtering these messages as spam. When the program detects a mailing-list letter it suggests user to add its address to Safe Recipients List. After that all messages from this list will be treated as legitimate.
Spam and Not-Spam Dictionaries - To adjust Spam Reader filtering rules to a user's personal needs it is possible to define custom Spam and Not-Spam Dictionaries. If a message contains a word or phrase from Spam Dictionary, then it is considered as Spam. Messages containing a word or phrases from Not-Spam Dictionary will be directed to Inbox without further anti-spam filtering.