NAME
bogofilter - fast Bayesian spam filter
SYNOPSIS
bogofilter [help options | classification options |
registration options | parameter options | info options]
[general options] [config file options]
where
help options are:
[-h] [--help] [-V] [-Q]
classification options are:
[-p] [-e] [-t] [-T] [-u] [-H] [-M] [-b] [-B object ...] [-R]
[general options] [parameter options] [config file options]
registration options are:
[-s | -n] [-S | -N] [general options]
general options are:
[-c filename] [-C] [-d dir] [-k cachesize] [-l] [-L tag] [-I filename]
[-O filename]
parameter options are:
[-E value[,value]] [-m value[,value][,value]] [-o value[,value]]
info options are:
[-v] [-y date] [-D] [-x flags]
config file options are:
[--option=value]
Note: Use bogofilter --help to display the complete list of options.
DESCRIPTION
Bogofilter is a Bayesian spam filter. In its normal mode of operation,
it takes an email message or other text on standard input, does a
statistical check against lists of "good" and "bad" words, and returns
a status code indicating whether or not the message is spam.
Bogofilter is designed with a fast algorithm, uses the Berkeley DB for
fast startup and lookups, coded directly in C, and tuned for speed, so
it can be used for production by sites that process a lot of mail.
THEORY OF OPERATION
Bogofilter treats its input as a bag of tokens. Each token is checked
against a wordlist, which maintains counts of the numbers of times it
has occurred in non-spam and spam mails. These numbers are used to
compute an estimate of the probability that a message in which the
token occurs is spam. Those are combined to indicate whether the
message is spam or ham.
While this method sounds crude compared to the more usual
pattern-matching approach, it turns out to be extremely effective. Paul
Graham´s paper A Plan For Spam[1] is recommended reading.
This program substantially improves on Paul´s proposal by doing smarter
lexical analysis. Bogofilter does proper MIME decoding and a
reasonable HTML parsing. Special kinds of tokens like hostnames and IP
addresses are retained as recognition features rather than broken up.
Various kinds of MTA cruft such as dates and message-IDs are ignored so
as not to bloat the wordlist. Tokens found in various header fields are
marked appropriately.
Another improvement is that this program offers Gary Robinson´s
suggested modifications to the calculations (see the parameters robx
and robs below). These modifications are described in Robinson´s paper
Spam Detection[2].
Since then, Robinson (see his Linux Journal article A Statistical
Approach to the Spam Problem[3]) and others have realized that the
calculation can be further optimized using Fisher´s method. Another
improvement[4] compensates for token redundancy by applying separate
effective size factors (ESF) to spam and nonspam probability
calculations.
In short, this is how it works: The estimates for the spam
probabilities of the individual tokens are combined using the "inverse
chi-square function". Its value indicates how badly the null hypothesis
that the message is just a random collection of independent words with
probabilities given by our previous estimates fails. This function is
very sensitive to small probabilities (hammish words), but not to high
probabilities (spammish words); so the value only indicates strong
hammish signs in a message. Now using inverse probabilities for the
tokens, the same computation is done again, giving an indicator that a
message looks strongly spammish. Finally, those two indicators are
subtracted (and scaled into a 0-1-interval). This combined indicator
(bogosity) is close to 0 if the signs for a hammish message are
stronger than for a spammish message and close to 1 if the situation is
the other way round. If signs for both are equally strong, the value
will be near 0.5. Since those message don´t give a clear indication
there is a tristate mode in bogofilter to mark those messages as
unsure, while the clear messages are marked as spam or ham,
respectively. In two-state mode, every message is marked as either spam
or ham.
Various parameters influence these calculations, the most important
are:
robx: the score given to a token which has not seen before. robx is the
probability that the token is spammish.
robs: a weight on robx which moves the probability of a little seen
token towards robx.
min-dev: a minimum distance from .5 for tokens to use in the
calculation. Only tokens farther away from 0.5 than this value are
used.
spam-cutoff: messages with scores greater than or equal to will be
marked as spam.
ham-cutoff: If zero or spam-cutoff, all messages with values strictly
below spam-cutoff are marked as ham, all others as spam (two-state).
Else values less than or equal to ham-cutoff are marked as ham,
messages with values strictly between ham-cutoff and spam-cutoff are
marked as unsure; the rest as spam (tristate)
sp-esf: the effective size factor (ESF) for spam.
ns-esf: the ESF for nonspam. These ESF values default to 1.0, which is
the same as not using ESF in the calculation. Values suitable to a
user´s email population can be determined with the aid of the bogotune
program.
OPTIONS
HELP OPTIONS
The -h option prints the help message and exits.
The -V option prints the version number and exits.
The -Q (query) option prints bogofilter´s configuration, i.e.
registration parameters, parsing options, bogofilter directory, etc.
CLASSIFICATION OPTIONS
The -p (passthrough) option outputs the message with an X-Bogosity line
at the end of the message header. This requires keeping the entire
message in memory when it´s read from stdin (or from a pipe or socket).
If the message is read from a file that can be rewound, bogofilter will
read it a second time.
The -e (embed) option tells bogofilter to exit with code 0 if the
message can be classified, i.e. if there is not an error. Normally
bogofilter uses different codes for spam, ham, and unsure
classifications, but this simplifies using bogofilter with procmail or
maildrop.
The -t (terse) option tells bogofilter to print an abbreviated
spamicity message containing 1 letter and the score. Spam is indicated
with "Y", ham by "N", and unsure by "U". Note: the formatting can be
customized using the config file.
The -T provides an invariant terse mode for scripts to use. bogofilter
will print an abbreviated spamicity message containing 1 letter and the
score. Spam is indicated with "S", ham by "H", and unsure by "U".
The -TT provides an invariant terse mode for scripts to use.
Bogofilter prints only the score and displays it to 16 significant
digits.
The -u option tells bogofilter to register the message´s text after
classifying it as spam or non-spam. A spam message will be registered
on the spamlist and a non-spam message on the goodlist. If the
classification is "unsure", the message will not be registered.
Effectively this option runs bogofilter with the -s or -n flag, as
appropriate. Caution is urged in the use of this capability, as any
classification errors bogofilter may make will be preserved and will
accumulate until manually corrected with the -Sn and -Ns option
combinations. Note this option causes the database to be opened for
write access, which can entail massive slowdowns through lock
contention and synchronous I/O operations.
The -H option tells bogofilter to not tag tokens from the header. This
option is for testing, you should not use it in normal operation.
The -M option tells bogofilter to process its input as a mbox formatted
file. If the -v or -t option is also given, a spamicity line will be
printed for each message.
The -b (streaming bulk mode) option tells bogofilter to classify
multiple objects whose names are read from stdin. If the -v or -t
option is also given, bogofilter will print a line giving file name and
classification information for each file. This is an alternative to -B
which lists objects on the command line.
An object in this context shall be a maildir (autodetected), or if it´s
not a maildir, a single mail unless -M is given - in that case it´s
processed as mbox. (The Content-Length: header is not taken into
account currently.)
When reading mbox format, bogofilter relies on the empty line after a
mail. If needed, formail -es will ensure this is the case.
The -B object ... (bulk mode) option tells bogofilter to classify
multiple objects named on the command line. The objects may be
filenames (for single messages), mailboxes (files with multiple
messages), or directories (of maildir and MH format). If the -v or -t
option is also given, bogofilter will print a line giving file name and
classification information for each file. This is an alternative to -b
which lists objects on stdin.
The -R option tells bogofilter to output an R data frame in text form
on the standard output. See the section on integration with R, below,
for further detail.
REGISTRATION OPTIONS
The -s option tells bogofilter to register the text presented as spam.
The database is created if absent.
The -n option tells bogofilter to register the text presented as
non-spam.
Bogofilter doesn´t detect if a message registered twice. If you do this
by accident, the token counts will off by 1 from what you really want
and the corresponding spam scores will be slightly off. Given a large
number of tokens and messages in the wordlist, this doesn´t matter. The
problem can be corrected by using the -S option or the -N option.
The -S option tells bogofilter to undo a prior registration of the same
message as spam. If a message was incorrectly entered as spam by -s or
-u and you want to remove it and enter it as non-spam, use -Sn. If -S
is used for a message that wasn´t registered as spam, the counts will
still be decremented.
The -N option tells bogofilter to undo a prior registration of the same
message as non-spam. If a message was incorrectly entered as non-spam
by -n or -u and you want to remove it and enter it as spam, then use
-Ns. If -N is used for a message that wasn´t registered as non-spam,
the counts will still be decremented.
GENERAL OPTIONS
The -c filename option tells bogofilter to read the config file named.
The -C option prevents bogofilter from reading configuration files.
The -d dir option allows you to set the directory for the database. See
the ENVIRONMENT section for other directory setting options.
The -k cachesize option sets the cache size for the BerkeleyDB
subsystem, in units of 1 MiB (1,048,576 bytes). Properly sizing the
cache improves bogofilter´s performance. The recommended size is one
third of the size of the database file. You can run the bogotune script
(in the tuning directory) to determine the recommended size.
The -l option writes an informational line to the system log each time
bogofilter is run. The information logged depends on how bogofilter is
run.
The -L tag option configures a tag which can be included in the
information being logged by the -l option, but it requires a custom
format that includes the %l string for now. This option implies -l.
The -I filename option tells bogofilter to read its input from the
specified file, rather than from stdin.
The -O filename option tells bogofilter where to write its output in
passthrough mode. Note that this only works when -p is explicitly
given.
PARAMETER OPTIONS
The -E value[,value] option allows setting the sp-esf value and the
ns-esf value. With two values, both sp-esf and ns-esf are set. If only
one value is given, parameters are set as described in the note below.
The -m value[,value][,value] option allows setting the min-dev value
and, optionally, the robs and robx values. With three values, min-dev,
robs, and robx are all set. If fewer values are given, parameters are
set as described in the note below.
The -o value[,value] option allows setting the spam-cutoff ham-cutoff
values. With two values, both spam-cutoff and ham-cutoff are set. If
only one value is given, parameters are set as described in the note
below.
Note: All of these options allow fewer values to be provided. Values
can be skipped by using just the comma delimiter, in which case the
corresponding parameter(s) won´t be changed. If only the first value is
provided, then only the first parameter is set. Trailing values can be
skipped, in which case the corresponding parameters won´t be changed.
Within the parameter list, spaces are not allowed after commas.
INFO OPTIONS
The -v option produces a report to standard output on bogofilter´s
analysis of the input. Each additional v will increase the verbosity of
the output, up to a maximum of 4. With -vv, the report lists the tokens
with highest deviation from a mean of 0.5 association with spam.
Option -y date can be used to override the current date when
timestamping tokens. A value of zero (0) turns off timestamping.
The -D option redirects debug output to stdout.
The -x flags option allows setting of debug flags for printing debug
information. See header file debug.h for the list of usable flags.
CONFIG FILE OPTIONS
Using GNU longopt -- syntax, a config file´s name=value statement
becomes a command line´s --option=value. Use command bogofilter --help
for a list of options and see bogofilter.cf.example for more info on
them. For example to change the X-Bogosity header to "X-Spam-Header",
use:
--spam-header-name=X-Spam-Header
ENVIRONMENT
Bogofilter uses a database directory, which can be set in the config
file. If not set there, bogofilter will use the value of
BOGOFILTER_DIR. Both can be overridden by the -d dir option. If none of
that is available, bogofilter will use directory $HOME/.bogofilter.
CONFIGURATION
The bogofilter command line allows setting of many options that
determine how bogofilter operates. File /etc/bogofilter.cf can be used
to set additional parameters that affect its operation. File
/etc/bogofilter.cf.example has samples of all of the parameters. Status
and logging messages can be customized for each site.
RETURN VALUES
0 for spam; 1 for non-spam; 2 for unsure ; 3 for I/O or other errors.
If both -p and -e are used, the return values are: 0 for spam or
non-spam; 3 for I/O or other errors.
Error 3 usually means that the wordlist file bogofilter wants to read
at startup is missing or the hard disk has filled up in -p mode.
INTEGRATION WITH OTHER TOOLS
Use with procmail
The following recipe (a) spam-bins anything that bogofilter rates as
spam, (b) registers the words in messages rated as spam as such, and
(c) registers the words in messages rated as non-spam as such. With
this in place, it will normally only be necessary for the user to
intervene (with -Ns or -Sn) when bogofilter miscategorizes something.
# filter mail through bogofilter, tagging it as Ham, Spam, or Unsure,
# and updating the wordlist
:0fw
| bogofilter -u -e -p
# if bogofilter failed, return the mail to the queue;
# the MTA will retry to deliver it later
# 75 is the value for EX_TEMPFAIL in /usr/include/sysexits.h
:0e
{ EXITCODE=75 HOST }
# file the mail to spam-bogofilter if it´s spam.
:0:
* ^X-Bogosity: Spam, tests=bogofilter
spam-bogofilter
# file the mail to unsure-bogofilter
# if it´s neither ham nor spam.
:0:
* ^X-Bogosity: Unsure, tests=bogofilter
unsure-bogofilter
# With this recipe, you can train bogofilter starting with an empty
# wordlist. Be sure to check your unsure-folder regularly, take the
# messages out of it, classify them as ham (or spam), and use them to
# train bogofilter.
The following procmail rule will take mail on stdin and save it to file
spam if bogofilter thinks it´s spam:
:0HB:
* ? bogofilter
spam
and this similar rule will also register the tokens in the mail
according to the bogofilter classification:
:0HB:
* ? bogofilter -u
spam
If bogofilter fails (returning 3) the message will be treated as
non-spam.
This one is for maildrop, it automatically defers the mail and retries
later when the xfilter command fails, use this in your ~/.mailfilter:
xfilter "bogofilter -u -e -p"
if (/^X-Bogosity: Spam, tests=bogofilter/)
{
to "spam-bogofilter"
}
The following .muttrc lines will create mutt macros for dispatching
mail to bogofilter.
macro index d "<enter-command>unset wait_key\n\
<pipe-entry>bogofilter -n\n\
<enter-command>set wait_key\n\
<delete-message>" "delete message as non-spam"
macro index \ed "<enter-command>unset wait_key\n\
<pipe-entry>bogofilter -s\n\
<enter-command>set wait_key\n\
<delete-message>" "delete message as spam"
Integration with Mail Transport Agent (MTA)
1. bogofilter can also be integrated into an MTA to filter all
incoming mail. While the specific implementation is MTA dependent,
the general steps are as follows:
2. Install bogofilter on the mail server
3. Prime the bogofilter databases with a spam and non-spam corpus.
Since bogofilter will be serving a larger community, it is
important to prime it with a representative set of messages.
4. Set up the MTA to invoke bogofilter on each message. While this is
an MTA specific step, you´ll probably need to use the -p, -u, and
-e options.
5. Set up a mechanism for users to register spam/non-spam messages, as
well as to correct mis-classifications. The most generic solution
is to set up alias email addresses to which users bounce messages.
6. See the doc and contrib directories for more information.
Use of R to verify bogofilter´s calculations
The -R option tells bogofilter to generate an R data frame. The data
frame contains one row per token analyzed. Each such row contains the
token, the sum of its database "good" and "spam" counts, the "good"
count divided by the number of non-spam messages used to create the
training database, the "spam" count divided by the spam message count,
Robinson´s f(w) for the token, the natural logs of (1 - f(w)) and f(w),
and an indicator character (+ if the token´s f(w) value exceeded the
minimum deviation from 0.5, - if it didn´t). There is one additional
row at the end of the table that contains a label in the token field,
followed by the number of words actually used (the ones with +
indicators), Robinson´s P, Q, S, s and x values and the minimum
deviation.
The R data frame can be saved to a file and later read into an R
session (see the R project website[5] for information about the
mathematics package R). Provided with the bogofilter distribution is a
simple R script (file bogo.R) that can be used to verify bogofilter´s
calculations. Instructions for its use are included in the script in
the form of comments.
LOG MESSAGES
Bogofilter writes messages to the system log when the -l option is
used. What is written depends on which other flags are used.
A classification run will generate (we are not showing the date and
host part here):
bogofilter[1412]: X-Bogosity: Ham, spamicity=0.000227
bogofilter[1415]: X-Bogosity: Spam, spamicity=0.998918
Using -u to classify a message and update a wordlist will produce (one
a single line):
bogofilter[1426]: X-Bogosity: Spam, spamicity=0.998918,
register -s, 329 words, 1 messages
Registering words (-l and -s, -n, -S, or -N) will produce:
bogofilter[1440]: register-n, 255 words, 1 messages
A registration run (using -s, -n, -N, or -S) will generate messages
like:
bogofilter[17330]: register-n, 574 words, 3 messages
bogofilter[6244]: register-s, 1273 words, 4 messages
FILES
/etc/bogofilter.cf
System configuration file.
~/.bogofilter.cf
User configuration file.
~/.bogofilter/wordlist.db
Combined list of good and spam tokens.
AUTHOR
Eric S. Raymond esr@thyrsus.com.
David Relson relson@osagesoftware.com.
Matthias Andree matthias.andree@gmx.de.
Greg Louis glouis@dynamicro.on.ca.
For updates, see the bogofilter project page[6].
SEE ALSO
bogolexer(1), bogotune(1), bogoupgrade(1), bogoutil(1)
NOTES
1. A Plan For Spam
http://www.paulgraham.com/spam.html
2. Spam Detection
http://radio.weblogs.com/0101454/stories/2002/09/16/spamDetection.html
3. A Statistical Approach to the Spam Problem
http://www.linuxjournal.com/article/6467
4. Another improvement
http://www.garyrobinson.net/2004/04/improved%5fchi.html
5. the R project website
http://cran.r-project.org/
6. bogofilter project page
http://bogofilter.sourceforge.net/