How Anti-spam Program Works

Most common technique that most anti-spam program to find Spam e-mails is review method. The anti-spam program examines incoming email messages and determines whether the email message content contains a known predetermined profile of Spam. Something as if it asks a question “Does this message look as Spam?”, and based on that study the anti-spam program takes the final conclusion to allow the specified email message or move it to the junk email list. At best this approach can determine most of the Spam emails allowing only a few ones getting through.

The analysis technique is not as dependable as it looks. Whenever you loosen the rules, you will get more Spam mail in your inbox. Whenever the policies are too strict, the program will stop all the Spam with the chance to block some non-span email messages. A non-spam email falsely blocked as spam is referring as false positive. If you get numerous false positives, you will need to read all the Spam emails searching for good messages in the middle of the rubbish. But this totally confutes the necessity of anti-spam programs.

The main weakness of the analysis technique is that the filters that work very well today can stop working tomorrow. Spammers can as well try an anti-spam program to shape their email messages so that they will get around the filters. As far as the developers anti-spam programs get better, the spammers improve their spam methods. That is why we get V1agra and C1alis and a lot of some other misspelled phrases in spam emails. Some advanced methods allow spammers create an email message that appears in a different way to a human person than the equivalent message is read by a program, for instance, when an email message contains a text readable for the recipient but the program sees it as a picture. Such kind of spam emails is raising in fame for spammers now.

Another best technology in fighting spam e-mails and a step forward in the analysis methods is the Bayesian filters. Beforehand the determination if a specific email messages is legitimate or spam was made based on how this message was measured up against a common predetermined profile of Spam. The Bayesian filters audit your own inbox and build the databases of good and spam words based on your own incoming email messages. This makes potential to correct the Bayesian filters for every user as the idea of Spam is prejudiced to some extent. By telling the Bayesian filters that this message is spam and that one is legitimate, you instruct the filters to distinguish the emails that you personally believe to be spam.

White list and Blacklist do not solve the problem either. Adding trusted email addresses to the white list is not rather safe as it appears to be. Today computer virus is well-known to use the email addresses from different address books to fake the messages and send themselves out. It is not likely to add whole the email addresses or even email domains to the blacklist either.

Spammers do not use actual email addresses to send spam. Today you get a spam email from this domain and tomorrow you can receive the very same spam email from a different domain. If you blacklist all the spammers’ emails, this will consequence in the blacklist of an endless size and slower work of anti-spam software.

Simply put, the war between the spammers and their victims keeps going. Although there is no flawless solution for the Spam problem today, mind and keep your anti-spam filters in fighting trim.

By: Izwan Mad

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