You can retrieve this information by issuing a couple of commands from an MSDOS prompt on SpamFilter's server, using the "FIND" command to count the number of lines in SpamFilter's activity logfile that contain certain strings.

For example, to count the total number of email attempts received by SpamFilter, you can look for the number of lines that contain the text "Connection from". To do this, open an MSDOS prompt, navigate to SpamFilter's logfile directory, and issue the command:

D:\SpamFilter\logfiles>find /c /i "Connection from" 20101118.log

You will see the number of matching lines in the result - 26,031 email attempts in the above example.

You can then count the number of emails that SpamFilter forwarded to your email server - these are the "clean", non-spam emails that SpamFilter identified. This is done by looking for the number of lines that contain the text "was queued". The command in this case is:

In this example that number is 238.

The percentage of clean emails allowed is then (238/26031) * 100 = 0.9%

The number of emails blocked will be 100 - (% of clean emails), in the above case it's then 100% - 0.9% = 99.1%

The above numbers were actual numbers for the amount of emails we received yesterday at logsat.com. Please note that we receive a *lot* of spam, and are thus blocking a very high percentage of it. In other cases usually the amount of spam varies between 70%-90% of the total emails received (instead of the 99.1% we receive).

 

It's also possible that some of those 106 emails were actually whitelisted due to some of the whitelist filters, which would make the spam catch ratio even more impressive on your secondary 

Joking aside, that has to be one of the highest ratios we've seen in over 8 years!

I'm not sure what you mean by "cannot access a server". Please do note that to install and run SpamFilter you will need to have administrative rights on the server it is installed on.

To find out what spam was mis-identified as ham, the only way to be certain of that amount is to go thru each and every single email received visually, and manually identify what is spam and what is not for multiple users over a large period of time (ex. a day). Only a human being will be able to correctly determine with fair certainty (I say fair as even humans can sometimes not be sure if some emails are to be considered spam or not... ex mailing lists) what kind of emails have been received and thus their spam/clean ratio.