ZachWingo

Note: Since the creation of this list better solutions are now available. When I created this hostfile list there weren’t any good free solutions to blocking adult sites…but now you can get a free and more comprehensive solution by using Opendns.org for your dns server and it allows you to create a free account and then block adult sites. It works very well and it’s free so you should use that instead and leave your hostfile for blocking adware/spyware sites.

About 1 year ago I began working on a host file that would contain porn sites for the purpose of blocking them. This has been done for years now to block things like ads and spyware but no one has been willing to work on one for adult/porn sites.

To make this work all you have to do is go to C:\WINNT\system32\drivers and open the hostfile with notepad or any text editor. It’s important to note that this file does not have and extension. You can rename it so it has a .txt to make it easier to open but remember to remove the extension when your done. Currently the list contains over 16,000 porn host names. In other words, zachwingo.info and www.zachwingo.info are considered two different host names so you will see two entries for almost all domains. To see the list follow this link to the Host File

UPDATE: I never received an email from Bluehost but I did send several dozens emails to pillmartpharmacy.com telling them to stop spamming blogs. My comment spam on two of my blogs dropped by more than half. I hope to get a better response in the future from Bluehost which appears to have decent service and uptime. Next time I’ll spam Matt Heaton’s blog telling him to enforce the companies policies on spam.

I began receiving a large number of comment spam on several different blogs for cheap medication and diet supplements all from the same IP. I began looking up some of the sites and noticed that the major offender was hosted by Bluehost.com so I did the logical thing and created a ticket for their abuse department. I received an error so I sent an email to their abuse department which went through just fine. I gave very specific details on the sites involved, the IP address making the fake comments, and suggested they do a google search for the websites domain name. When you do a search of that domain name, you will get a return of literally tens of thousands of entries in blogs, guest books, and discussion boards. These are obviously spam because you get a short one line comment like, “nice site….” and a link back to their site.

I did not get a response from bluehost.com nor has the site been removed and I am still receiving spam comments from the site they host. The site is called pillmartpharmacy.com. Bluehost.com has an obligation to remove sites that are spamming themselves or affiliated advertising companies that do. They should have their account canceled immediately without question by the obvious spam seen by searching for their site. If I do not hear from bluehost.com within 72 hours of Monday, October 1st, I will begin asking my friends to cancel their accounts with bluehost and move to another host. I will offer to pay their first months hosting if needed to get them to switch. If you use bluehost I encourage you to complain about this type of service and switch to a better host like dreamhost or my favorite Media Temple.

Please click the Stop Supporting Spam link to let them know we want them to stop supporting spam by not hosting sites that do.

The Bayesian theorem is a mathematical probability formula. Bayesian spam filtering applies that formula to spam filtering to calculate the probability of of whether or not an email is spam. The idea of using the theory to filter email was first suggested by Mehran Sahami, a professor at Standford in a paper titled, A Bayesian Approach to Filtering Junk E-Mail.

The theory states 1:

the probability that an email is spam, given that it has certain words in it, is equal to the probability of finding those certain words in spam email, times the probability that any email is spam, divided by the probability of finding those words in any email.

To further complicate things, the Bayesian formula is one that learns from the habits of a particular user or multiple users. Let’s use an example from the GFI’s security lab 2:

This word probability is calculated as follows: If the word “mortgage” occurs in 400 of 3,000 spam mails and in 5 out of 300 legitimate emails, for example, then its spam probability would be 0.8889 (that is, [400/3000] divided by [5/300 + 400/3000]).

You can see by this formula that a mortgage broker would be more likely to have spam containing the word mortgage because the chances of finding the word “mortgage” in a legitimate email is higher than the emails of a lawyer or doctor. Also, let say a spammer begins to change words from “Viagra” to “V1agra” to evade filters that do a simply keyword search, the Bayesian filter would immediately catch it because the chance of using a word like, “V1agra” would be almost zero.

For these reasons the Bayesian filter can grow and adapt to the personal style and habits of the users. The important key here is that the more time and thus more email it scans the more accurate it will be. This method takes time but is very accurate and far superior to former methods of detecting spam. Hopefully this explains a little better why some filters seem to miss a lot of spam and some are very accurate.

1. Bayesian spam filtering, Mathematical Foundation

2. Why Bayesian filtering is the most effective anti-spam technology