We all hate spam (the unwanted e-mail... not the delicious mystery meat in a can). Most of us are probably using some sort of spam filter to reduce the amount of unwanted junk e-mail, but do you really understand what it does? Have you ever had someone insist that they sent you an e-mail, but you never got it? Or, have you ever sent several e-mail and not received a reply? Your spam filter (or the recipient’s) could be the culprit.
There are a few methods of identifying spam. The most common are the Bayesian filter, the Real-time Black List, and the White List Only. These three methods identify potential spam, but the other important factor is the action that is taken once e-mail is flagged. The action can be anything from completely blocking the e-mail to adding a *SPAM* tag to the subject line.
Used in combination, these anti-spam efforts can greatly reduce the amount of unwanted junk mail you received. But, if not used properly legitimate e-mails can be filtered along with the spam. As important as e-mail has become in transacting business, it is important to be aware of how your anti-spam settings work.
BAYESIAN FILTERING
A Bayesian filter tries to identify junk e-mail based on keywords in the subject line and header (among other identifiers). This works well if you, or your Internet Service Provider (ISP), have setup good keywords. Typically, the keyword list contains terms like: “Viagra”, “Cialis”, “Hoodia”, etc. Many other key words are used to identify e-mail sent from on-line gambling sites and those containing “adult” content.
When an e-mail comes in which contains one or more of the keywords it gets flagged as spam. However, the spammers are very good at modifying the text of their e-mail to avoid Baysian filtering. You may notice that you still get e-mails with creative “mis-spellings” such as CIAL1S (notice the number “1” in place of the “I”). The spammers know that “Cialis” is probably a keyword that your Bayesian filter is looking for so they choose a similar spelling that you will recognize but your spam filter will not.
To be effective, the Bayesian filter must anticipate these variations in spelling. Even then, a Bayesian filter cannot block common words that other well-known spam contains because those same terms may be in legitimate e-mails. The spam that offers great rates on refinancing your home is a prime example. As title professionals, the terms in those unwanted solicitations are very common in business e-mails exchanged with clients; such as “homeowner, refinance, payment, etc.”
Therefore, it is important that you understand the keywords your spam filter is looking for. The last thing you want is your clients’ e-mails to be blocked.
REAL-TIME BLACK LISTS
Another method is the Real-time Black List (RBL). RBL checks are done on the IP address of the sender and if the list contains that IP address the e-mail is flagged as spam. However, these require some determination of which IP addresses make the list. In the case of dynamic IP addresses (those that are changing often) a spammer could have an IP address that he uses to send out mass amounts spam and that IP address could get reassigned to a non-spamming user. Once the IP address gets added to a RBL, it is very difficult to get it removed.
You may not even know that your IP address is on one of these lists. In which case, anyone using a RBL to filter spam will not get your e-mail. You can check to see if your IP address is on a list at www.robtex.com. Equally problematic is when you are using a RBL filter and you have clients on one of the lists. Their e-mail to you may get blocked without you ever knowing.