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E-mail filter and classifier based on Bayesian learning
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SpamOracle -- a spam classification tool Version 1.6 OVERVIEW: SpamOracle is a tool to help detect and filter away "spam" (unsolicited commercial e-mail). It proceeds by statistical analysis of the words that appear in the e-mail, comparing the frequencies of words with those found in a user-provided corpus of known spam and known legitimate e-mail. The classification algorithm is based on Bayes' formula, and is described in Paul Graham's paper, "A plan for spam", http://www.paulgraham.com/spam.html. This program is designed to work in conjunction with procmail. The result of the analysis is output as an additional message header "X-Spam:", followed by "yes", "no" or "unknown", plus additional details. A procmail rule can then test this "X-Spam:" header and deliver the e-mail to the appropriate mailbox. In addition, SpamOracle also also analyses MIME attachments, extracting relevant information such as MIME type, character encoding and attached file name, and summarizing them in an additional "X-Attachments:" header. This allows procmail to easily reject e-mails containing suspicious attachments, e.g. Windows executables which often indicate a virus. LICENSE: This program is distributed under the terms of the GPL (GNU Public License) version 2, available from http://www.gnu.org/licenses/gpl.txt. REQUIREMENTS AND LIMITATIONS: - To compile: Objective Caml, https://ocaml.org/, version 4.02 or later. - To use: . Your mail must be delivered to a Unix machine. You must have a shell account on this machine. This machine must have procmail (http://www.procmail.org/) installed. Your ~/.forward file must be set up to run all incoming e-mail through procmail. . To provide the corpus of messages from which SpamOracle "learns", an archive of at least 1000 of your e-mails is needed. The archive must be manually or semi-automatically split into known spams and known good messages. Mis-classified messages in the corpus (e.g. spams mistakenly stored among the good messages) will decrease the efficiency of the classification. The archive must be in Unix mailbox format, or in "one message per file" format (a la MH). Other formats, such as Emacs' Babyl, are not supported. . The notion of "word" used by SpamOracle is slanted towards Western European languages, i.e. the ISO Latin-1 and Latin-9 character sets. Preliminary support for JIS-encoded Japanese is provided. Still, SpamOracle will not work well if you receive many legitimate e-mails written in other character sets, such as Chinese or Korean. INSTALLATION: Edit the Makefile and change the definitions of the following variables at the top of the file: LANGUAGES the languages you're interested in besides English CPP how to invoke the C preprocessor BINDIR where to install the executable Do "make" in this directory. Become superuser if necessary and do "make install". INITIALIZATION: To build the database of word frequencies from the corpus, do: rm ~/.spamoracle.db spamoracle add -v -good goodmails -spam spammails (By default, the database is stored in the file ".spamoracle.db" in your home directory. This can be overriden with the -f option: spamoracle -f mydatabase add ... ) This assumes that the good, non-spam messages from the corpus are stored in the file "goodmails", and the known spam messages in the file "spammails". You can also fetch corpus messages from several files: spamoracle add -v -good goodmails1 ... goodmailsN \ -spam spammails1 ... spammailsP To check that the database was built correctly, and familiarize yourself with the statistical analysis performed by SpamOracle, invoke the "test" mode on the mailboxes that you just used for building the corpus: spamoracle test goodmails | more spamoracle test spammails | more For each message in the given mailboxes, you'll see a summary like this: From: bbo <firstname.lastname@example.org> Subject: Check This Out Score: 1.00 -- 15 Details: refid:98 $$$$:98 surfing:98 asp:95 click:93 cable:92 instantly:90 https:88 internet:87 www:86 U4:85 isn't:14 month:81 com:75 surf:75 Attachments: cset="GB2312" type="application/octet-stream" name="Guangwen4.zip" The first two lines are just the From: and Subject: fields of the original message. The "Score:" line summarizes the result of the analysis. The first number (between 0.0 and 1.0) is the probability that the message is actually spam --- or, equivalently, the degree of similarity of the message with the spam messages in the corpus. The second number (an integer between 0 and 15) is the number of "interesting" words found in the message. "Interesting" words are those that occur at least 5 times in the corpus. In the example, we have 15 interesting words (the maximum) and a score of 1.00, indicating a spam with high certainty. The "Details:" line provides an explanation of the score. It lists the 15 most interesting words found in the message, that is, the 15 interesting words whose probability of denoting a spam is farthest away from the neutral 0.5. Each word is given with its individual score, written as a percentage (between 01 and 99) rather than as a probability so as to save space. Here, we see a number of very "spammish" words such as "$$$$" or "click", with probability 0.98 and 0.93 respectively, and a few "innocent" words such as "isn't" (probability 0.14). The "U4" word with probability 0.85 is actually a pseudo-word representing a 4-letter word all in uppercase -- something spammers are fond of. The "Attachments:" line summarizes some information about MIME attachments for this message. Here, we have one attachment of type "application/octect-stream", file name "Guangwen4.zip", and character set "GB2312". The latter is an encoding for Chinese and a solid hint that this is a Chinese spam (assuming that, like me, you can't read Chinese). Normally, when running "spamoracle test goodmails", most messages should come out with low score (0.2 or less), and when running "spamoracle test spammails", most messages should come out with a high score (0.8 or more). If not, your corpus isn't very good, or not well classified into spam and non-spam. To quickly see the outliers, you can reduce the interval of scores for which message summaries are displayed, as follows: spamoracle test -min 0.2 goodmails | more # Shows only good mails with score >= 0.2 spamoracle test -max 0.8 spammails | more # Shows only spam mails with score <= 0.8 Now, for a more challenging test, take a mailbox that contains unfiltered e-mails, i.e. a mixture of spam and legitimate e-mails, and run it through "spamoracle": spamoracle test mymailbox | less Marvel at how well the oracle recognizes spam from the rest! If the result isn't that marvelous to you, keep in mind that certain spams are just too short to be recognized (not enough significant words). Also, perhaps your corpus was too small, or not well categorized... USING SPAMORACLE AND PROCMAIL TO FILTER YOUR INCOMING E-MAIL: Once the database is built, you're ready to run incoming e-mails through SpamOracle. The command "spamoracle mark" reads one e-mail from standard input, and copies it to standard output, with two headers inserted: "X-Spam:" and "X-Attachments:". The X-Spam: header is as follows: X-Spam: yes; <score>; <details> or X-Spam: no; <score>; <details> or X-Spam: unknown; <score>; <details> The <score> and <details> are as described for "spamoracle test". The "yes" / "no" / "unknown" synthesizes the results of the analysis: "yes": score >= 0.8 and at least 5 interesting words found "no": score <= 0.2 and at least 5 interesting words found "unknown": otherwise The "unknown" case generally occurs for very short messages, where not enough interesting words were found. The "X-Attachments:" header contains the same information as the "Attachments:" output of "spamoracle test", that is, a summary of the message attachments. To process automatically your incoming e-mail through "spamoracle" and act upon the results of the analysis, just insert the following "recipes" in the file ~/.procmailrc: :0fw | /usr/local/bin/spamoracle mark :0 * ^X-Spam: yes; spambox What these cryptic commands mean is: - Run every mail through the "spamoracle mark" command. (If spamoracle wasn't installed in /usr/local/bin, adjust the path as necessary.) This adds two headers to the message: "X-Spam:" and "X-Attachments:", describing the results of the spam analysis and the attachment analysis. - If we have an "X-Spam: yes" header, deliver the message to the file "spambox" rather than to your regular mailbox. Presumably, you'll read "spambox" once in a while, but less often than your regular mailbox. Daring users can put "/dev/null" instead of "spambox" to just throw away the message, but please don't do that until you've used SpamOracle for a while and are happy with the results. SpamOracle's false positive rate (i.e. legitimate mails classified as spam) is low (0.1% on my mail) but not null. So, better save the presumed spams somewhere, and scan them quickly from time to time. If you'd like to enjoy a bit of attachment-based filtering, here are some procmail rules for that: :0 * ^X-Attachments:.*name=".*\.(pif|scr|exe|bat)" spambox :0 * ^X-Attachments:.*type="audio/(x-wav|x-midi) spambox :0 * ^(Content-type:.*|X-Attachments:.*cset="|^Subject:.*=\?)(ks_c|gb2312|iso-2|euc-|big5|windows-1251) spambox The first rule treats as spam every mail that has a Windows executable as attachment. These mails are typically sent by viruses. The second rule does the same with attachments of type x-wav or x-midi. I never normally receive music by e-mail, however some popular e-mail viruses seem fond of these attachment types. The third rule treats as spam every mail that uses character encodings corresponding to Korean, Chinese, Japanese, and Cyrillic. Since I don't read any of these scripts, why not get rid of the messages immediately? UPDATING THE DATABASE: At any time, you can add more known spams or known legitimate messages to the database by using the "spamoracle add" command. For instance, if you find a spam message that was not classified as such, run it through "spamoracle add -spam", so that SpamOracle can learn from its mistake. (Without additional arguments, this command will read a single message from standard input and record it as spam.) Under mutt for instance, just highlight the spam message and type |spamoracle add -spam Similarly, if you find a legitimate message while checking your spam box, run it through "spamoracle add -good". Another option is to collect more known spams or more known good messages into mailbox files, and once in a while do spamoracle add -good new_good_mails or spamoracle add -spam new_spam_mails TECHNICAL DETAILS: SpamOracle's notion of "word" is the following: - any run of 3 to 12 letters, single quotes, dashes (-) - any run of 3 to 8 digits, dots, commas, dollar, euro and percent signs. If support for non-English european languages was compiled in, letters also include the relevant accented letters for the languages in question. All words are mapped to lowercase, and accented letters are mapped to the corresponding non-accented letters. In addition, a run of three or more uppercase letters generates a pseudo-word "Un" where n is the length of the run. Similarly, a run of three or more non-ASCII characters (code >= 128) generates a pseudo-word "Wn" where n is the length of the run. For instance, the following text: SUMMER in English is written "été" in French ¹²³ is processed into the following words, assuming French support was selected: U5 summer english written ete french W3 and if French support was not selected: U5 summer english written french W3 For your edification and entertainment, the contents of the database can be dumped with the "spamoracle list <regexp>" command, where <regexp> is an Emacs-style regexp specifying the words you're interested in, e.g.: spamoracle list '.*' # show all words -- big list! spamoracle list 'sex.*' spamoracle list 'caml.*' It is possible to tweak many of the parameters that govern filtering via the configuration file ~/.spamoracle.conf. The configurable parameters are listed and explained in the man page spamoracle.conf (5). All parameters have reasonable default values, but you may try to tweak them to get better filtering.
E-mail filter and classifier based on Bayesian learning
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