SpamProbe
Details
| Size: | 256 KB |
| Last Update: | 2008-02-14 06:01:23 |
| Version: | 1.4d |
| OS Support: | 256 KB |
| License/Program Type: | Freeware |
| Publisher: | Brian Burton |
| Price: | $0.00 |
Description:
SpamProbe is a fast, intelligent, automatic spam detector
using Paul Graham style Bayesian analysis of word counts in spam
and non-spam emails. Filtering adapts to personal tastes
automatically. No manual rule creation is required. SpamProbe is
intended for use with procmail and maild.
SpamProbe operates on a different basis entirely. Instead of
using pattern matching and a set of human generated rules SpamProbe
relies on a Bayesian analysis of the frequency of words used in
spam and non-spam emails received by an individual person. The
process is completely automatic and tailors itself to the kinds of
emails that each person receives.
SpamProbe is known to compile and run on a wide range of nix
systems including Mac OS X, Darwin, Linux (RedHat and Debian),
FreeBSD, Solaris, and AIX. SpamProbe can also be compiled to run on
Windows under the Cygwin environment.
NOTE: SpamProbe is released under the Qt Public License
(QPL).
Here are some key features of
"SpamProbe":
· Spam detection using Bayesian analysis of terms
contained in each email. Words used often in spams but not in good
email tend to indicate that a message is spam. Generally over 90%
effective at detecting spam once a few hundred spams have been
classified. My personal database is over 99% effective.
· Automatically learns from incoming mails as they are
classified. Incorporates user's feedback to tailor classification
to each user's personal tastes.
· Works with procmail, maildrop, or a similar tool to
produce a complete server or client side spam filtering system.
· Written in C++ for good performance. Database access
using Peter Graf's PBL ISAM library or Berkeley DB for quick
startup and fast term count retrieval. Also supports a fast, fixed
size hash file format for maximum speed or when a fixed size
database is essential.
· Recognition and decoding of MIME attachments in
quoted-printable and base64 encoding. Automatically skips non-text
attachments. MIME decoding enables SpamProbe to make decisions
based on words in the emails rather than base64 gobbledigook.
· Analyzes image attachments to derive useful
information from them. This feature allows SpamProbe to detect
spams that contain an image and little to no text content.
· Counts two word phrases as well as single words for
higher precision. Can easily be configured to use longer phrases if
desired.
· Ignores HTML tags in emails for scoring purposes
unless the -h command line option is used. Many spams use HTML and
few humans do so HTML tends to become a powerful recognizer of
spams. However in the author's opinion this also substantially
increases the likelihood of false positives if someone does send a
non-spam emai containing HTML tags. SpamProbe does pull urls from
inside of html tags however since those tend to be spammer
specific.
· Locks mboxes and databases using fcntl file locking to
avoid problems when multiple emails arrive simultaneously.
· Scores only the Received, Subject, To, From, and Cc
headers. All other headers are ignored to make it hard for spammers
to hide non-spammy words in X- headers to fool the filter. The -H
command line option can be used to override this.
· Natively supports mbox, MBX, and Maildir mail box
formats.
· Supports Content-Length: field in mbox headers. This
can be disabled using -Y option to use only From_ to recognize new
messages.
· Uses MD5 hash of emails to recognize reclassification
of an already classified spam to avoid distortion of the word
counts if emails are reclassified. This way emails can be kept in a
mailbox that is repeatedly scanned by spamprobe without counting
them more than once.
· Provides a date stamp based database cleanup command
to remove terms from the database if their counts never rise above
a certain threshold value (normally 2).
· Provides an edit-term command allowing users to
directly modify the counts of individual terms. For example to
force a particular term to be considered spammy or good.
0 comments
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