diff options
Diffstat (limited to 'mail/dspam/DETAILS')
-rwxr-xr-x | mail/dspam/DETAILS | 39 |
1 files changed, 24 insertions, 15 deletions
diff --git a/mail/dspam/DETAILS b/mail/dspam/DETAILS index 73c8b52e3f..18fd8045ce 100755 --- a/mail/dspam/DETAILS +++ b/mail/dspam/DETAILS @@ -32,24 +32,33 @@ fi KEYWORDS="email mail spam filter" SHORT='server-side anti-spam agent for UNIX email servers' cat << EOF -DSPAM is a scalable and open-source content-based spam filter designed for -multi-user enterprise systems. On a properly configured system, many users -experience results between 99.5% - 99.95%, or one error for every 200 to 2000 -messages. DSPAM supports many different MTAs and can also be deployed as a -stand-alone SMTP appliance. For developers, the DSPAM core engine (libdspam) can -be easily incorporated directly into applications for drop-in filtering (GPL +DSPAM is a scalable and open-source content-based spam filter designed for +multi-user enterprise systems. On a properly configured system, many users +experience results between 99.5% - 99.95%, or one error for every 200 to 2000 +messages. DSPAM supports many different MTAs and can also be deployed as a +stand-alone SMTP appliance. For developers, the DSPAM core engine (libdspam) +can +be easily incorporated directly into applications for drop-in filtering (GPL applies; commercial licenses are also available). -DSPAM has been implemented on many large and small scale systems with the largest -being reported at about 350,000 mailboxes. It is presently being used or planned +DSPAM has been implemented on many large and small scale systems with +the largest +being reported at about 350,000 mailboxes. It is presently being used +or planned for use in multiple commercial solutions. -DSPAM is an adaptive filter which means it is capable of learning and adapting to -each user's email. Instead of working off of a list of "rules" to identify spam, -DSPAM's probabilistic engine examines the content of each message and learns what -type of content the user deems as spam (or nonspam). This approach to +DSPAM is an adaptive filter which means it is capable of learning and +adapting to +each user's email. Instead of working off of a list of "rules" to identify +spam, +DSPAM's probabilistic engine examines the content of each message and +learns what +type of content the user deems as spam (or nonspam). This approach to machine-learning provides much higher levels of accuracy than commercial -"hodge-podge" solutions, and with minimal resources. DSPAM's best recorded levels -of accuracy have included 99.991% by one avid user (2 errors in 22,786) and 99.987% -by the author (1 error in 7000), which is ten times more accurate than a human being! +"hodge-podge" solutions, and with minimal resources. DSPAM's best recorded +levels +of accuracy have included 99.991% by one avid user (2 errors in 22,786) +and 99.987% +by the author (1 error in 7000), which is ten times more accurate than a +human being! EOF |