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       bogofilter - fast Bayesian spam filter


       bogofilter [help options | classification options |
                  registration options | parameter options | info options]
                  [general options] [config file options]


       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:


       Note: Use bogofilter --help to display the complete list of options.


       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.


       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

       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

       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

       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



       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.


       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

       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

       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.


       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

       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.


       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

       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


       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

       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.


       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.


       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 for more info on
       them. For example to change the X-Bogosity header to "X-Spam-Header",



       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.


       The bogofilter command line allows setting of many options that
       determine how bogofilter operates. File /etc/ can be used
       to set additional parameters that affect its operation. File
       /etc/ has samples of all of the parameters. Status
       and logging messages can be customized for each site.


       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.


       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

           | 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

           { EXITCODE=75 HOST }

           # file the mail to spam-bogofilter if it´s spam.

           * ^X-Bogosity: Spam, tests=bogofilter

           # file the mail to unsure-bogofilter
           # if it´s neither ham nor spam.

           * ^X-Bogosity: Unsure, tests=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:

           * ? bogofilter

       and this similar rule will also register the tokens in the mail
       according to the bogofilter classification:

           * ? bogofilter -u

       If bogofilter fails (returning 3) the message will be treated as

       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

       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.


       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

           bogofilter[17330]: register-n, 574 words, 3 messages
           bogofilter[6244]: register-s, 1273 words, 4 messages


           System configuration file.

           User configuration file.

           Combined list of good and spam tokens.


           Eric S. Raymond
           David Relson
           Matthias Andree
           Greg Louis

       For updates, see the bogofilter project page[6].


       bogolexer(1), bogotune(1), bogoupgrade(1), bogoutil(1)


        1. A Plan For Spam

        2. Spam Detection

        3. A Statistical Approach to the Spam Problem

        4. Another improvement

        5. the R project website

        6. bogofilter project page