Most new e-mail clients include their own basic form of antispam or Junk Mail filtering. Microsoft Outlook 2003 comes with a fairly comprehensive Junk E-Mail filter that gives you some control over how your mail is managed.

Configuring Outlook Junk E-Mail

To access the filter options, open Outlook and go to Actions > Junk E-Mail > Junk E-Mail Options. From this dialog box you can manage how Outlook responds to incoming e-mails. The first tab, Options, instructs Outlook as to how sensitive spam detection should be. There are actually only two levels of sensitivity, the remaining two options allowing the filter to be turned off or configured for preapproved senders only. It's generally a good idea to start off with sensitivity set to Low, because it's easier to mark spam that the filter misses than to locate legitimate e-mails in a folder full of spam. Figure 4-3 shows the available options:

Figure 4-3: Outlook 2003 Junk E-mail Options.

At the bottom of the dialog box is a checkbox labeled Permanently delete suspected junk e-mail. You should never check this because Outlook can occasionally determine a legitimate e-mail to be false positive spam e-mail; if it's deleted permanently, you'll never even know it was incorrectly detected. This becomes important when you begin to train the filtering system.

The remaining tabs in the Junk E-mail Options dialog are self-explanatory, and allow you to configure e-mail addresses that either should always be let through the filter, or should always be blocked.

After enabling the Junk E-mail filter, Outlook begins to detect spam. Initially, you may find that some legitimate e-mails are being flagged as spam, so you need to tell Outlook what it has done wrong. Simply open the legitimate e-mail and select Actions > Junk E-mail > Mark as Not Junk. Outlook then modifies its spam detection rules accordingly.

Bayesian Filtering

Outlook 2003 is the first version of Outlook that comes with a generally decent spam filtering system, but it's still a long way from perfect. If you receive a lot of spam or find that the Outlook filtering isn't doing the job, you may need to upgrade to full Bayesian filtering.

Bayesian filtering is a system that has a fairly complex implementation, but is easy to understand in principle. It's effectively a self-learning filtering system. Bayesian filtering uses some complex mathematical probability calculations to decide whether the e-mail being scanned is spam or not spam. It bases the final decision on the contents of all the legitimate e-mails and all of the spam you've received up to that point. This is an extremely effective filtering system, because it constantly learns and adapts to the e-mail you receive.

As a practical example, imagine you're a keen tropical fish keeper. You've signed up for tropical fish information newsletters, which you receive weekly. When you install your Bayesian filtering, you show it a list of spam and a list of ham so it can build its initial database. In the list of ham were a number of fish newsletters, each containing some information on a medicine called Methylene Blue. The following week, you receive another newsletter. When the Bayesian filter scans the letter, it notices that Methylene Blue has shown up a couple of times in the ham e-mails, and not at all in the spam e-mails. On this basis, it lets the e-mail through.

Now imagine a friend of yours has also installed a Bayesian filter, but he doesn't like tropical fish. When he configured the filter he supplied it with e-mails containing the phrase Methylene Blue that he considered to be spam. Whenever your friend receives the tropical fish newsletter, the Bayesian filter sees that Methylene Blue shows up in spam e-mails a number of times so the e-mail is blocked.

In practice, the decision to block or allow e-mail is based on more than a single string of text in the message, but the same principle applies. By learning your e-mail habits, Bayesian filtering can effectively reduce the spam you receive by more than 99 percent. Part of the reason it's so effective is because it relies on you, the end user, to teach it what to do. Much like a small child, you tell the filter what's good, what's bad, and if it gets something wrong you let it know what it did wrong.