Provided by: bogofilter-common_1.2.4+dfsg1-12_amd64 
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/