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  1. Feb 26 08:20:22 selse spamd[1762]: spamd: connection from 127.0.0.1 [127.0.0.1]:59530 to port 783, fd 5
  2. Feb 26 08:20:22 selse spamd[1762]: spamd: setuid to Debian-exim succeeded
  3. Feb 26 08:20:22 selse spamd[1762]: spamd: checking message <E1lFYMI-0003aE-2H@example.com> for Debian-exim:107
  4. Feb 26 08:20:23 selse spamd[1762]: bayes: cannot write to /var/lib/spamassassin/bayes_db/bayes_journal, bayes db update ignored: Permission denied
  5. Feb 26 08:20:23 selse spamd[1762]: spamd: clean message (1.4/5.0) for Debian-exim:107 in 0.9 seconds, 2225 bytes.
  6. Feb 26 08:20:23 selse spamd[1762]: spamd: result: . 1 - BAYES_50,DKIM_INVALID,DKIM_SIGNED,KHOP_HELO_FCRDNS,SPF_HELO_NONE,SPF_NONE,URIBL_BLOCKED scant
  7. ime=0.9,size=2225,user=Debian-exim,uid=107,required_score=5.0,rhost=127.0.0.1,raddr=127.0.0.1,rport=59530,mid=<E1lFYMI-0003aE-2H@example.com>,bayes=0.492334,autolearn=no autolearn_force=no
  8. Feb 26 08:20:23 selse spamd[1761]: prefork: child states: II
  9.  
  10.  
  11. #########################################
  12.  
  13. jenny@selse:/etc/spamassassin$ ls -al /var/lib/spamassassin/bayes_db/
  14. total 16940
  15. drwxrwxr-x 2 debian-spamd debian-spamd 4096 Feb 26 09:15 .
  16. drwxr-xr-x 9 debian-spamd debian-spamd 4096 Nov 23 2018 ..
  17. -rwxrwxr-x 1 debian-spamd debian-spamd 20119552 Feb 25 21:15 bayes_seen
  18. -rw-rw-rw- 1 root root 5464064 Feb 26 09:15 bayes_toks
  19.  
  20. #########################################
  21.  
  22. X-SA-Exim-Connect-IP: 50.3.251.79
  23. X-SA-Exim-Mail-From: north@healthyadvice.cyou
  24. X-Spam-Checker-Version: SpamAssassin 3.4.2 (2018-09-13) on
  25. example.com
  26. X-Spam-Level: ****
  27. X-Spam-Status: No, score=4.6 required=4.9 tests=AWL,BAYES_50,
  28. FROM_SUSPICIOUS_NTLD,FROM_SUSPICIOUS_NTLD_FP,HTML_MESSAGE,
  29. HTML_MIME_NO_HTML_TAG,MIME_HTML_ONLY,MIME_QP_LONG_LINE,
  30. PDS_OTHER_BAD_TLD,RCVD_IN_MSPIKE_BL,RCVD_IN_MSPIKE_L5,RDNS_NONE
  31. autolearn=no autolearn_force=no version=3.4.2
  32. Subject: Visceral Fat Pushes Your Stomach Out - DISCOVERY
  33. X-SA-Exim-Version: 4.2.1 (built Wed, 08 May 2019 21:11:16 +0000)
  34. X-Spam_score: 4.4
  35. X-Spam_score_int: 44
  36. X-Spam_bar: ++++
  37. X-Spam_report: Spam detection software, running on the system "example.com",
  38. has NOT identified this incoming email as spam.
  39.  
  40. ###############################################
  41.  
  42. jenny@selse:/etc/spamassassin$ cat local.cf
  43. # This is the right place to customize your installation of SpamAssassin.
  44. #
  45. # See 'perldoc Mail::SpamAssassin::Conf' for details of what can be
  46. # tweaked.
  47. #
  48. # Only a small subset of options are listed below
  49. #
  50. ###########################################################################
  51.  
  52. # Add *****SPAM***** to the Subject header of spam e-mails
  53. #
  54. rewrite_header Subject *****SPAM*****
  55.  
  56.  
  57. # Save spam messages as a message/rfc822 MIME attachment instead of
  58. # modifying the original message (0: off, 2: use text/plain instead)
  59. #
  60. # report_safe 1
  61.  
  62.  
  63. # Set which networks or hosts are considered 'trusted' by your mail
  64. # server (i.e. not spammers)
  65. #
  66. # trusted_networks 212.17.35.
  67.  
  68.  
  69. # Set file-locking method (flock is not safe over NFS, but is faster)
  70. #
  71. # lock_method flock
  72.  
  73.  
  74. # Set the threshold at which a message is considered spam (default: 5.0)
  75. #
  76. # required_score 5.0
  77.  
  78.  
  79. # Use Bayesian classifier (default: 1)
  80. #
  81. use_bayes 1
  82.  
  83.  
  84. # Bayesian classifier auto-learning (default: 1)
  85. #
  86. bayes_auto_learn 1
  87.  
  88. bayes_path /var/lib/spamassassin/bayes_db/bayes
  89. bayes_file_mode 0777
  90.  
  91. # Set headers which may provide inappropriate cues to the Bayesian
  92. # classifier
  93. #
  94. bayes_ignore_header X-Bogosity
  95. bayes_ignore_header X-Spam-Flag
  96. bayes_ignore_header X-Spam-Status
  97.  
  98.  
  99. # Some shortcircuiting, if the plugin is enabled
  100. #
  101. ifplugin Mail::SpamAssassin::Plugin::Shortcircuit
  102. #
  103. # default: strongly-whitelisted mails are *really* whitelisted now, if the
  104. # shortcircuiting plugin is active, causing early exit to save CPU load.
  105. # Uncomment to turn this on
  106. #
  107. shortcircuit USER_IN_WHITELIST on
  108. shortcircuit USER_IN_DEF_WHITELIST on
  109. shortcircuit USER_IN_ALL_SPAM_TO on
  110. shortcircuit SUBJECT_IN_WHITELIST on
  111.  
  112. # the opposite; blacklisted mails can also save CPU
  113. #
  114. shortcircuit USER_IN_BLACKLIST on
  115. shortcircuit USER_IN_BLACKLIST_TO on
  116. shortcircuit SUBJECT_IN_BLACKLIST on
  117.  
  118. # if you have taken the time to correctly specify your "trusted_networks",
  119. # this is another good way to save CPU
  120. #
  121. # shortcircuit ALL_TRUSTED on
  122.  
  123. # and a well-trained bayes DB can save running rules, too
  124. #
  125. shortcircuit BAYES_99 spam
  126. shortcircuit BAYES_00 ham
  127.  
  128. endif # Mail::SpamAssassin::Plugin::Shortcircuit
  129.  
  130. ###################################################
  131.  
  132. jenny@selse:/etc/spamassassin$ cat v310.pre
  133.  
  134.  
  135. # DCC - perform DCC message checks.
  136. #
  137. # DCC is disabled here because it is not open source. See the DCC
  138. # license for more details.
  139. #
  140. #loadplugin Mail::SpamAssassin::Plugin::DCC
  141.  
  142. # Pyzor - perform Pyzor message checks.
  143. #
  144. loadplugin Mail::SpamAssassin::Plugin::Pyzor
  145.  
  146. # Razor2 - perform Razor2 message checks.
  147. #
  148. loadplugin Mail::SpamAssassin::Plugin::Razor2
  149.  
  150. # SpamCop - perform SpamCop message reporting
  151. #
  152. loadplugin Mail::SpamAssassin::Plugin::SpamCop
  153.  
  154. # AntiVirus - some simple anti-virus checks, this is not a replacement
  155. # for an anti-virus filter like Clam AntiVirus
  156. #
  157. #loadplugin Mail::SpamAssassin::Plugin::AntiVirus
  158.  
  159. # AWL - do auto-whitelist checks
  160. #
  161. #loadplugin Mail::SpamAssassin::Plugin::AWL
  162.  
  163. # AutoLearnThreshold - threshold-based discriminator for Bayes auto-learning
  164. #
  165. loadplugin Mail::SpamAssassin::Plugin::AutoLearnThreshold
  166.  
  167. # TextCat - language guesser
  168. #
  169. #loadplugin Mail::SpamAssassin::Plugin::TextCat
  170.  
  171. # AccessDB - lookup from-addresses in access database
  172. #
  173. #loadplugin Mail::SpamAssassin::Plugin::AccessDB
  174.  
  175. # WhitelistSubject - Whitelist/Blacklist certain subject regular expressions
  176. #
  177. loadplugin Mail::SpamAssassin::Plugin::WhiteListSubject
  178.  
  179. ###########################################################################
  180. # experimental plugins
  181.  
  182. # DomainKeys - perform DomainKeys verification
  183. #
  184. # This plugin has been removed as of v3.3.0. Use the DKIM plugin instead,
  185. # which supports both Domain Keys and DKIM.
  186.  
  187. # MIMEHeader - apply regexp rules against MIME headers in the message
  188. #
  189. loadplugin Mail::SpamAssassin::Plugin::MIMEHeader
  190.  
  191. # ReplaceTags
  192. #
  193. loadplugin Mail::SpamAssassin::Plugin::ReplaceTags
  194.  
  195. ##############################################################
  196.  
  197. jenny@selse:/etc/spamassassin$ cat v320.pre
  198.  
  199.  
  200. # Check - Provides main check functionality
  201. #
  202. loadplugin Mail::SpamAssassin::Plugin::Check
  203.  
  204. # HTTPSMismatch - find URI mismatches between href and anchor text
  205. #
  206. loadplugin Mail::SpamAssassin::Plugin::HTTPSMismatch
  207.  
  208. # URIDetail - test URIs using detailed URI information
  209. #
  210. loadplugin Mail::SpamAssassin::Plugin::URIDetail
  211.  
  212. # Shortcircuit - stop evaluation early if high-accuracy rules fire
  213. #
  214. # loadplugin Mail::SpamAssassin::Plugin::Shortcircuit
  215.  
  216. # Plugins which used to be EvalTests.pm
  217. # broken out into separate plugins
  218. loadplugin Mail::SpamAssassin::Plugin::Bayes
  219. loadplugin Mail::SpamAssassin::Plugin::BodyEval
  220. loadplugin Mail::SpamAssassin::Plugin::DNSEval
  221. loadplugin Mail::SpamAssassin::Plugin::HTMLEval
  222. loadplugin Mail::SpamAssassin::Plugin::HeaderEval
  223. loadplugin Mail::SpamAssassin::Plugin::MIMEEval
  224. loadplugin Mail::SpamAssassin::Plugin::RelayEval
  225. loadplugin Mail::SpamAssassin::Plugin::URIEval
  226. loadplugin Mail::SpamAssassin::Plugin::WLBLEval
  227.  
  228. # VBounce - anti-bounce-message rules, see rules/20_vbounce.cf
  229. #
  230. loadplugin Mail::SpamAssassin::Plugin::VBounce
  231.  
  232. # Rule2XSBody - speedup by compilation of ruleset to native code
  233. #
  234. # loadplugin Mail::SpamAssassin::Plugin::Rule2XSBody
  235.  
  236. # ASN - Look up the Autonomous System Number of the connecting IP
  237. # and create a header containing ASN data for bayes tokenization.
  238. # See plugin's POD docs for usage info.
  239. #
  240. # loadplugin Mail::SpamAssassin::Plugin::ASN
  241.  
  242. # ImageInfo - rules to match metadata of image attachments
  243. #
  244. loadplugin Mail::SpamAssassin::Plugin::ImageInfo
  245.  
  246.  
  247. #############################################
  248.  
  249. BINFILE=/usr/bin/sa-learn
  250.  
  251. learnbox()
  252. {
  253. #echo "Learning from $1's mailbox $3"
  254. MAILPREFIX=/home/$1/Maildir/.
  255.  
  256. if $BINFILE --$2 ${MAILPREFIX}$3/cur/ & $BINFILE --$2 ${MAILPREFIX}$3/new/
  257. # if su $1 --command="$BINFILE --$2 ${MAILPREFIX}$3/cur/" & su $1 --command="$BINFILE --$2 ${MAILPREFIX}$3/new/"
  258.  
  259. then if [ "$4" = "clean" ]
  260. then
  261.  
  262. rm -f ${MAILPREFIX}$3/cur/*
  263. rm -f ${MAILPREFIX}$3/new/*
  264.  
  265. fi
  266. fi
  267. }
  268.  
  269. learnbox jenny spam missed-spam clean
  270.  
  271. #######################################################
  272.  
  273. Learning from jenny's mailbox missed-spam
  274. Learned tokens from 0 message(s) (0 message(s) examined)
  275. Learned tokens from 0 message(s) (0 message(s) examined)
  276.  
  277.  
  278.  
  279.  
  280.  
  281.  
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