sa-summary is a script for summarising spam log files. Statistics on rules, spamassassin scores, Bayes scores, time spent processing mail and file size of mails are provided.
Summary tables are provided for:
- Totals
- Score, time, size, rules and Bayes - total, min, max, mean
- Rules hit:
- top spam rules
- top ham rules
- top negative spam rules
- top positive ham rules
- Mail details:
- marginal mail classifications
- spam with low Bayes scores
- ham with high Bayes scores
- spam with high positive rule scores
- ham with high positive rule scores
If you like sa-summary, give it a star, or fork it and contribute!
Here are some examples of usage with short command line arguments:
# Print help message
./sa-summary -h
# Analyse contents of spam.log in current directory
./sa-summary -f spam.log
# Analyse contents of multiple spam.log.* files under /var/log directory
./sa-summary -l /var/log -f spam.log.*# Only display top 5 rules
./sa-summary -f spam.log -t 5
# Order first 4 tables by TOTSCO
./sa-summary -f spam.log -o TOTSCO
# Use higher positive spamassassin score threshold
./sa-summary -f spam.log -p 3
# Use lower negative spamassassin score threshold
./sa-summary -f spam.log -n -1
# Use lower margin with marginal mail classifications table
./sa-summary -f spam.log -m 2
# Print web friendly output
./sa-summary -f spam.log -wLong command line options are listed in the Details section below.
Requires perl 5.8 or later, spamassassin 3.4.2 and procmail.
The following should work in any unix like environment:
wget https://raw.githubusercontent.com/makeyourownmaker/sa-summary/master/sa-summary
chmod u+x sa-summary
./sa-summary -hI generate a custom spam log file configured mostly within procmail.
One day I will write an ode to procmail ... and possibly a diss track for sieve :-)
My full spamassassin procmail file is included here spamassassin.procmail.
It's included in $HOME/.procmailrc like so:
########################################################################
# Spamassassin
# WARNING: Put as few recipes as possible above the spam recipes
INCLUDERC=$HOME/.procmail/spamassassin.procmail
# End of spamassassin recipes
########################################################################
If you are uncomfortable with procmail recipes and custom log files then see the Alternatives section below.
The following fields are included:
| Field | Description |
|---|---|
| Date time | Basic timestamp |
| Stop time | Time spamassassin finished processing at (secs since epoch) |
| Start time | Time spamassassin started processing at (secs since epoch) |
| Mail size | Size of mail in bytes |
| Message ID | Email header message ID |
| Language | 2 character language code(s) |
| Classification result | Yes or no (spam or ham) |
| Spamassassin score | Aggregate score (5 or more is spam) |
| Rules | Spamassassin rules hit (with rule scores) |
| Autolearn | Was mail used for Bayesian learning |
| Bayes score | Bayes score for mail (0 to 1) |
Fields are tab separated.
Here are a few examples:
2019.09.20-12:24:04 1568982244 1568982233 1050 <54ad4096-0989-83dd-988f-ba5c9c84fa4a@bristol.ac.uk> en No, score=-3.6 required=5.0 tests=BAYES_00,MAILING_LIST_MULTI,RCVD_IN_DNSWL_LOW,SPF_HELO_PASS,SPF_PASS autolearn=ham autolearn_force=no version=3.4.2 0.0000
2019.09.20-12:46:34 1568983594 1568983585 6436 <D9183B21-940E-4884-8D47-64D9AE1C6635@exeter.ac.uk> en No, score=-3.6 required=5.0 tests=BAYES_00,LOTS_OF_MONEY,MAILING_LIST_MULTI,RCVD_IN_DNSWL_LOW,SPF_HELO_PASS,SPF_PASS autolearn=ham autolearn_force=no version=3.4.2 0.0000
2019.09.20-13:02:14 1568984534 1568984526 1230 <CAPVnE2YCHktS2n9oQMCXgn39yYhWf5Y-gxXLwKw_SV_PQ1fT5A@mail.gmail.com> en No, score=-2.7 required=5.0 tests=AWL,BAYES_00,DKIM_ADSP_CUSTOM_MED,FREEMAIL_FORGED_FROMDOMAIN,FREEMAIL_FROM,HEADER_FROM_DIFFERENT_DOMAINS,MAILING_LIST_MULTI,RCVD_IN_DNSWL_LOW,SPF_HELO_PASS,SPF_PASS autolearn=ham autolearn_force=no version=3.4.2 0.0000
2019.09.20-14:31:04 1568989864 1568989855 6042 <DB7PR01MB5080A86AE6EDD7479248F96CB7880@DB7PR01MB5080.eurprd01.prod.exchangelabs.com> en No, score=-3.5 required=5.0 tests=AWL,BAYES_00,HEADER_FROM_DIFFERENT_DOMAINS,LOTS_OF_MONEY,MAILING_LIST_MULTI,RCVD_IN_DNSWL_LOW,SPF_HELO_PASS,SPF_PASS autolearn=ham autolearn_force=no version=3.4.2 0.0000
2019.09.20-15:15:40 1568992540 1568992531 4676 <74eefd0fdef841d8806b169b6a6505de@uwaterloo.ca> en No, score=-3.6 required=5.0 tests=AWL,BAYES_00,HEADER_FROM_DIFFERENT_DOMAINS,LOTS_OF_MONEY,MAILING_LIST_MULTI,RCVD_IN_DNSWL_LOW,SPF_HELO_PASS,SPF_PASS autolearn=ham autolearn_force=no version=3.4.2 0.0000
This script is based on sa-stats.pl by Dallas Engelken. The original script has been extensively modified.
I use it for processing a custom generated spamassassin log file. It no longer works with the default spamassassin log files.
The sa-summary tool proved very useful while I was seeing too many false-positive spamassassin classifications.
The following tables are provided:
- Totals
- Score, time, size, rules and Bayes - total, min, average, max
Mails
Email: 351 Autolearn: 181
Spam: 110 Autolearn: 96
Ham: 241 Autolearn: 85
Duration
Email: From: Sun May 28 07:50:52 2017 To: Wed Jun 7 08:21:27 2017
Spam: From: Sun May 28 07:50:52 2017 To: Wed Jun 7 08:01:48 2017
Ham: From: Sun May 28 08:39:10 2017 To: Wed Jun 7 08:21:27 2017
Score
Email: Total: -761.60 Min: -107.30 Avg: -2.17 Max: 81.00
Spam: Total: 3012.40 Min: 5.00 Avg: 27.39 Max: 81.00
Ham: Total: -3774.00 Min: -107.30 Avg: -15.66 Max: 4.60
Time
Email: Total: 1.44 hours Min: 3.00 sec Avg: 14.73 sec Max: 953.00 sec
Spam: Total: 0.27 hours Min: 4.00 sec Avg: 8.77 sec Max: 22.00 sec
Ham: Total: 1.17 hours Min: 3.00 sec Avg: 17.44 sec Max: 953.00 sec
Size
Email: Total: 5810915 bytes Min: 0.00 bytes Avg: 16555.31 bytes Max: 249624.00 bytes
Spam: Total: 1531391 bytes Min: 95.00 bytes Avg: 13921.74 bytes Max: 101431.00 bytes
Ham: Total: 4279524 bytes Min: 0.00 bytes Avg: 17757.36 bytes Max: 249624.00 bytes
Rules
Email: Total: 4654 Unique: 281 Min: 2 Avg: 13.26 Max: 38
Spam: Total: 2101 Unique: 178 Min: 5 Avg: 19.10 Max: 38
Ham: Total: 2553 Unique: 103 Min: 2 Avg: 10.59 Max: 28
Bayes
Email: Total: 88.54 Min: 0.00 Avg: 0.25 Max: 1.00
Spam: Total: 82.02 Min: 0.00 Avg: 0.75 Max: 1.00
Ham: Total: 6.52 Min: 0.00 Avg: 0.03 Max: 0.52
- Rules hit:
- top spam rules
- Sorted by COUNT by default
- top spam rules
------------------------------------------------------------------------------------------------
RANK RULE NAME COUNT %OFMAIL %OFSPAM %OFHAM AVGSCO SCORE TOTSCO
------------------------------------------------------------------------------------------------
1 HTML_MESSAGE 105 67.24 95.45 54.36 0.00 0.0010 0.1050
2 RDNS_NONE 82 25.07 74.55 2.49 4.50 4.5000 369.0000
3 RAZOR2_CHECK 75 21.65 68.18 0.41 1.90 1.9000 142.5000
4 RAZOR2_CF_RANGE_E8_51_100 75 21.65 68.18 0.41 1.89 1.8860 141.4500
5 RAZOR2_CF_RANGE_51_100 75 21.65 68.18 0.41 0.50 0.5000 37.5000
------------------------------------------------------------------------------------------------
- top ham rules
- Sorted by COUNT by default
------------------------------------------------------------------------------------------------
RANK RULE NAME COUNT %OFMAIL %OFSPAM %OFHAM AVGSCO SCORE TOTSCO
------------------------------------------------------------------------------------------------
1 BAYES_00 228 65.53 1.82 94.61 -1.90 -1.9000 -433.2000
2 SPF_PASS 171 51.85 10.00 70.95 -0.00 -0.0010 -0.1710
3 AWL 170 49.00 1.82 70.54 0.42 0.2110 35.8700
4 RCVD_IN_HOSTKARMA_W 163 46.44 0.00 67.63 -0.10 -0.1000 -16.3000
5 KHOP_RCVD_TRUST 161 45.87 0.00 66.80 -1.75 -1.7500 -281.7500
------------------------------------------------------------------------------------------------
- top negative spam rules
- Sorted by TOTSCO by default
----------------------------------------------------------------------------------------------
RANK RULE NAME COUNT %OFMAIL %OFSPAM %OFHAM AVGSCO SCORE TOTSCO
----------------------------------------------------------------------------------------------
1 KHOP_DNSBL_ADJ 22 6.55 20.00 0.41 -3.30 -3.3000 -72.6000
2 BAYES_00 2 65.53 1.82 94.61 -1.90 -1.9000 -3.8000
3 CRM114_GOOD 1 36.47 0.91 52.70 -3.00 -3.0000 -3.0000
4 CRM114_PROB_GOOD 4 11.68 3.64 15.35 -0.50 -0.5000 -2.0000
5 DKIM_VALID_AU 8 30.48 7.27 41.08 -0.10 -0.1000 -0.8000
----------------------------------------------------------------------------------------------
- top positive ham rules
- Sorted by TOTSCO by default
----------------------------------------------------------------------------------------------
RANK RULE NAME COUNT %OFMAIL %OFSPAM %OFHAM AVGSCO SCORE TOTSCO
----------------------------------------------------------------------------------------------
1 DCC_CHECK 54 32.19 53.64 22.41 3.00 3.0000 162.0000
2 KAM_COUK 47 14.53 3.64 19.50 1.10 1.1000 51.7000
3 RDNS_NONE 6 25.07 74.55 2.49 4.50 4.5000 27.0000
4 PYZOR_CHECK 7 10.26 26.36 2.90 2.50 2.5000 17.5000
5 KAM_NUMSUBJECT 33 11.68 7.27 13.69 0.50 0.5000 16.5000
----------------------------------------------------------------------------------------------
- Mail details:
- marginal mail classifications
- Sorted by DIFF by default
- DIFF - Difference between spamassassin score and threshold value (usually 5)
- RESULT - Yes for spam, No for ham
- marginal mail classifications
--------------------------------------------------------------------------------
MESSAGE_ID
SCORE DIFF RESULT TIME SIZE BAYES LANG
RULES
--------------------------------------------------------------------------------
<KtmxD25vLTdcI85l7p-Ywu_jX1_eMx5ptOIi9EDBMPA.lwau0udeD_0Y7xkbGfPr2h6q3wgt62ufQAuLVKAoP-g@erefen.info>
5.0 0 Yes 8 secs 12456 bytes 0.1545 en
BAYES_20=-0.001,HTML_MESSAGE=0.001,KAM_INFOUSMEBIZ=0.75,RAZOR2_CF_RANGE_51_100=0.5,RAZOR2_CF_RANGE_E8_51_100=1.886,RAZOR2_CHECK=1.9,T_REMOTE_IMAGE=0.01,URIBL_BLOCKED=0.001
<upKpFbW5DtiS0Yzd_At-AE9I45-maPlttBv2EEP12rQ.LzNKojy1dgaB_4dF1h0fgvDpjiOopZQVJffm01461g8@erefen.info>
5.0 0 Yes 8 secs 12033 bytes 0.3477 en
BAYES_40=-0.001,HTML_MESSAGE=0.001,KAM_INFOUSMEBIZ=0.75,RAZOR2_CF_RANGE_51_100=0.5,RAZOR2_CF_RANGE_E8_51_100=1.886,RAZOR2_CHECK=1.9,T_REMOTE_IMAGE=0.01,URIBL_BLOCKED=0.001
<DcIXYhyaoHT88UzfIPCneS7lMWzcpeqF-wN63RctNP4.L6ad7Z-fxGQPZMjR5MpzoyXFpt9O4pOj22L8VCAYYgg@refuti.info>
4.6 0.4 No 5 secs 8786 bytes 0.5004 en
BAYES_50=0.8,CRM114_PROB_SPAM=0.5,HTML_MESSAGE=0.001,KAM_INFOUSMEBIZ=0.75,PYZOR_CHECK=2.5,T_REMOTE_IMAGE=0.01,URIBL_BLOCKED=0.001
<20170529235902.3DE8C10195@foo.org>
4.3 0.7 No 18 secs 10577 bytes 0.5000 en da
AWL=-4.007,BAYES_50=0.8,DKIM_SIGNED=0.1,DKIM_VALID=-0.1,DKIM_VALID_AU=-0.1,KAM_ASCII_DIVIDERS=0.8,KAM_MXURI=1.5,NO_RELAYS=-0.001,URIBL_ABUSE_SURBL=1.25,URIBL_DBL_SPAM=2.5,URIBL_RHS_DOB=1.514
<0AhDuulGhopstSnhQbMJRmKIWld-9zYVcYcuJPtn6I4.1TlePfoiiUAM3tAMSMsHE5tVF69Y4WmLqC8OmVN3KeY@diunap.info>
5.8 0.8 Yes 6 secs 17387 bytes 0.4992 en
BAYES_50=0.8,HTML_FONT_LOW_CONTRAST=0.001,HTML_MESSAGE=0.001,KAM_INFOUSMEBIZ=0.75,RAZOR2_CF_RANGE_51_100=0.5,RAZOR2_CF_RANGE_E8_51_100=1.886,RAZOR2_CHECK=1.9,URIBL_BLOCKED=0.001
--------------------------------------------------------------------------------
- spam with low Bayes scores
- Sorted by BAYES by default
- DIFF - Difference between spamassassin score and threshold value (usually 5)
- RESULT - Yes for spam, No for ham
--------------------------------------------------------------------------------
MESSAGE_ID
SCORE DIFF RESULT TIME SIZE BAYES
--------------------------------------------------------------------------------
<20170602024104.95763.84760.98D42E82@grovesidecapitalmanagement.co.uk>
25.8 20.8 Yes 14 secs 88880 bytes 0.0000
<178d98100b64db3b6cbbeaf_db1b2e59@underdo.sinet.co>
12.7 7.7 Yes 5 secs 2500 bytes 0.0000
<CrQuZn1JkWtf_aopZSZzvRJlnonkNuRTUisfkXin25I.GGph59fufibRc_pK26XeG9NISrN0Ghx9V8IEgt0sPBc@rhdfol.info>
12.1 7.1 Yes 6 secs 11334 bytes 0.0273
<KtmxD25vLTdcI85l7p-Ywu_jX1_eMx5ptOIi9EDBMPA.lwau0udeD_0Y7xkbGfPr2h6q3wgt62ufQAuLVKAoP-g@erefen.info>
5.0 0 Yes 8 secs 12456 bytes 0.1545
<1784bc9ba7540b3c6cbbeaf_b2453759@bookmaking.netrep.co>
16.0 11 Yes 7 secs 2825 bytes 0.2142
--------------------------------------------------------------------------------
- ham with high Bayes scores
- Sorted by BAYES by default
- DIFF - Difference between spamassassin score and threshold value (usually 5)
- RESULT - Yes for spam, No for ham
--------------------------------------------------------------------------------
MESSAGE_ID
SCORE DIFF RESULT TIME SIZE BAYES
--------------------------------------------------------------------------------
<20170528235902.2FA64101A6@bar.org>
3.5 1.5 No 9 secs 8192 bytes 0.5003
<DcIXYhyaoHT88UzfIPCneS7lMWzcpeqF-wN63RctNP4.L6ad7Z-fxGQPZMjR5MpzoyXFpt9O4pOj22L8VCAYYgg@refuti.info>
4.6 0.4 No 5 secs 8786 bytes 0.5004
<20170604235901.C133810D63@baz.org>
0.9 4.1 No 11 secs 9700 bytes 0.5021
<145CE01219A8AEEBA31FEDE65751145C@WRG3UW5>
-85.3 90.3 No 6 secs 1590 bytes 0.5193
--------------------------------------------------------------------------------
- spam with high positive rule scores
- Sorted by NEG by default
- DIFF - Difference between spamassassin score and threshold value (usually 5)
- POS - Sum of all the positive rule scores
- NEG - Sum of all the negative rule scores
--------------------------------------------------------------------------------
MESSAGE_ID
SCORE DIFF POS NEG TIME SIZE BAYES
RULES
--------------------------------------------------------------------------------
<592F197D.8471.C7602F@qux.corge.org>
12.8 7.8 29.135 -16.368 11 secs 1923 bytes 0.5000
AWL=-13.068,BAYES_50=0.8,CK_HELO_DYNAMIC_SPLIT_IP=0.001,DATE_IN_PAST_03_06=1.592,HELO_DYNAMIC_IPADDR2=3.607,HTML_MESSAGE=0.001,KHOP_DNSBL_ADJ=-3.3,KHOP_SC_CIDR8=0.1,RCVD_IN_BRBL_LASTEXT=3,RCVD_IN_HOSTKARMA_BL=1.7,RCVD_IN_MSPIKE_BL=0.01,RCVD_IN_MSPIKE_L5=0.001,RCVD_IN_PSBL=2.7,RCVD_IN_SBL_CSS=3.335,RCVD_IN_SEMBLACK=0.5,RCVD_IN_XBL=3,RDNS_NONE=4.5,SPF_SOFTFAIL=0.665,TO_EQ_FM_DIRECT_MX=0.622,TVD_RCVD_IP=0.001,URI_WP_HACKED=3
<458633w2sukg$0672b6v2$1824g7h0@qfn>
39.8 34.8 43.588 -3.8 9 secs 12030 bytes 0.9974
BAYES_99=4,CRM114_PROB_GOOD=-0.5,HK_RANDOM_ENVFROM=0.001,HK_RANDOM_FROM=0.001,HTML_IMAGE_RATIO_04=0.556,HTML_MESSAGE=0.001,INVALID_DATE=1.096,KHOP_DNSBL_ADJ=-3.3,KHOP_URIBL_BUMP=2.5,MIME_HTML_ONLY=0.723,RAZOR2_CF_RANGE_51_100=0.5,RAZOR2_CF_RANGE_E8_51_100=1.886,RAZOR2_CHECK=1.9,RCVD_IN_BL_SPAMCOP_NET=1.347,RCVD_IN_BRBL_LASTEXT=3,RCVD_IN_HOSTKARMA_BL=1.7,RCVD_IN_MSPIKE_BL=0.01,RCVD_IN_MSPIKE_L5=0.001,RCVD_IN_NIX_SPAM=0.25,RCVD_IN_PSBL=2.7,RCVD_IN_RP_RNBL=1.31,RCVD_IN_SBL_CSS=3.335,RCVD_IN_SEMBLACK=0.5,RCVD_IN_SORBS_SPAM=0.5,RCVD_IN_SORBS_WEB=1.5,RCVD_IN_XBL=3,RCVD_VIA_APNIC=0.001,RDNS_NONE=4.5,SPF_HELO_SOFTFAIL=0.732,SPF_SOFTFAIL=0.665,URIBL_ABUSE_SURBL=1.25,URIBL_DBL_SPAM=2.5,URIBL_SBL=1.623
<kly6_fbu1-5000402557-5550386327-40-86226.6784400517@rfl8>
33.6 28.6 37.422 -3.8 5 secs 12007 bytes 1.0000
BAYES_99=4,BAYES_999=4,CRM114_PROB_GOOD=-0.5,DIGEST_MULTIPLE=0.293,HTML_IMAGE_RATIO_04=0.556,HTML_MESSAGE=0.001,INVALID_DATE=1.096,KHOP_DNSBL_ADJ=-3.3,MIME_HTML_ONLY=0.723,PYZOR_CHECK=2.5,RAZOR2_CF_RANGE_51_100=0.5,RAZOR2_CF_RANGE_E8_51_100=1.886,RAZOR2_CHECK=1.9,RCVD_IN_BL_SPAMCOP_NET=1.347,RCVD_IN_BRBL_LASTEXT=3,RCVD_IN_HOSTKARMA_BL=1.7,RCVD_IN_MSPIKE_BL=0.01,RCVD_IN_MSPIKE_L5=0.001,RCVD_IN_NIX_SPAM=0.25,RCVD_IN_PSBL=2.7,RCVD_IN_RP_RNBL=1.31,RCVD_IN_SEMBLACK=0.5,RCVD_IN_SORBS_SPAM=0.5,RCVD_IN_SORBS_WEB=1.5,RCVD_VIA_APNIC=0.001,RDNS_NONE=4.5,SPF_HELO_SOFTFAIL=0.732,SPF_SOFTFAIL=0.665,URIBL_ABUSE_SURBL=1.25,URIBL_BLOCKED=0.001
<033545z0beu5$zb1r5k25$7065x8v2@LO260628153036>
41.7 36.7 44.983 -3.3 13 secs 235 bytes 0.9996
BAYES_99=4,BAYES_999=4,DCC_CHECK=3,DIGEST_MULTIPLE=0.293,HTML_MESSAGE=0.001,HTML_MIME_NO_HTML_TAG=0.377,INVALID_DATE=1.096,KAM_LAZY_DOMAIN_SECURITY=1,KHOP_DNSBL_ADJ=-3.3,MIME_HTML_ONLY=0.723,RAZOR2_CF_RANGE_51_100=0.5,RAZOR2_CF_RANGE_E8_51_100=1.886,RAZOR2_CHECK=1.9,RCVD_IN_BL_SPAMCOP_NET=1.347,RCVD_IN_BRBL_LASTEXT=3,RCVD_IN_HOSTKARMA_BL=1.7,RCVD_IN_MSPIKE_BL=2,RCVD_IN_MSPIKE_L5=0.8,RCVD_IN_PSBL=2.7,RCVD_IN_RP_RNBL=1.31,RCVD_IN_XBL=3,RCVD_VIA_APNIC=0.001,RDNS_NONE=4.5,TO_MALFORMED=2.099,URIBL_ABUSE_SURBL=1.25,URIBL_DBL_PHISH=2.5
<26b470f7vt4j$sz3l56t7$fo4332v8@FRCI08>
31.3 26.3 34.635 -3.3 9 secs 330 bytes 0.9691
BAYES_95=3,DCC_CHECK=3,DIGEST_MULTIPLE=0.293,HTML_MESSAGE=0.001,HTML_MIME_NO_HTML_TAG=0.377,INVALID_DATE=1.096,KAM_LAZY_DOMAIN_SECURITY=1,KHOP_DNSBL_ADJ=-3.3,MIME_HTML_ONLY=0.723,RAZOR2_CF_RANGE_51_100=0.5,RAZOR2_CF_RANGE_E8_51_100=1.886,RAZOR2_CHECK=1.9,RCVD_IN_BL_SPAMCOP_NET=1.347,RCVD_IN_BRBL_LASTEXT=3,RCVD_IN_HOSTKARMA_BL=1.7,RCVD_IN_MSPIKE_BL=2,RCVD_IN_MSPIKE_L5=0.8,RCVD_IN_NIX_SPAM=0.25,RCVD_IN_PSBL=2.7,RCVD_IN_RP_RNBL=1.31,RCVD_IN_SEMBLACK=0.5,RCVD_IN_SORBS_WEB=1.5,RCVD_VIA_APNIC=0.001,RDNS_NONE=4.5,URIBL_ABUSE_SURBL=1.25,URIBL_BLOCKED=0.001
--------------------------------------------------------------------------------
- ham with high positive rule scores
- Sorted by POS by default
- DIFF - Difference between spamassassin score and threshold value (usually 5)
- POS - Sum of all the positive rule scores
- NEG - Sum of all the negative rule scores
--------------------------------------------------------------------------------
MESSAGE_ID
SCORE DIFF POS NEG TIME SIZE BAYES
RULES
--------------------------------------------------------------------------------
<DB5PR05MB13035B73AEE1E607674860EFEECB0@DB5PR05MB1303.eurprd05.prod.outlook.com>
-6.7 11.7 2.007 -8.751 10 secs 4259 bytes 0.0000
BAYES_00=-1.9,CRM114_GOOD=-3,KAM_NUMSUBJECT=0.5,KHOP_RCVD_TRUST=-1.75,RCVD_IN_HOSTKARMA_W=-0.1,RCVD_IN_HOSTKARMA_WL=-2,SPF_PASS=-0.001,SUBJ_ALL_CAPS=1.506,URIBL_BLOCKED=0.001
<0100015c542d318d-e408f5ff-df97-41b2-9219-021ac6f5e88e-000000@email.amazonses.com>
-3.9 8.9 2.015 -5.9521 5 secs 3480 bytes 0.0000
AWL=-0.000,BAYES_00=-1.9,DKIM_SIGNED=0.1,DKIM_VALID=-0.1,DKIM_VALID_AU=-0.1,HTML_IMAGE_ONLY_28=1.404,HTML_MESSAGE=0.001,KAM_NUMSUBJECT=0.5,KHOP_RCVD_TRUST=-1.75,RCVD_IN_DNSWL_NONE=-0.0001,RCVD_IN_HOSTKARMA_W=-0.1,RCVD_IN_HOSTKARMA_WL=-2,RP_MATCHES_RCVD=-0.001,SPF_PASS=-0.001,T_REMOTE_IMAGE=0.01
<E1dH22r-000wOE-kb@ppsw-40.csi.cam.ac.uk>
-2.9 7.9 2.022 -4.903 8 secs 41189 bytes 0.0000
AWL=1.286,BAYES_00=-1.9,CRM114_GOOD=-3,HTML_MESSAGE=0.001,LOTS_OF_MONEY=0.001,MIME_HTML_ONLY=0.723,RP_MATCHES_RCVD=-0.001,SPF_HELO_PASS=-0.001,SPF_PASS=-0.001,T_REMOTE_IMAGE=0.01,URIBL_BLOCKED=0.001
<B81F3D646AC0484CA7B9EED7B20F0DA10131DFB873@TS-EXMB-P1.CXG.CO.UK>
-6.7 11.7 2.087 -8.751 8 secs 12019 bytes 0.0000
AWL=2.084,BAYES_00=-1.9,CRM114_GOOD=-3,HTML_MESSAGE=0.001,KHOP_RCVD_TRUST=-1.75,MIME_QP_LONG_LINE=0.001,RCVD_IN_HOSTKARMA_W=-0.1,RCVD_IN_HOSTKARMA_WL=-2,SPF_PASS=-0.001,URIBL_BLOCKED=0.001
<10BB0CP1706071115320000001732@oswald1.endsleigh.co.uk>
-1.9 6.9 2.101 -4 6 secs 1770 bytes 0.0000
BAYES_00=-1.9,KAM_COUK=1.1,KAM_NUMSUBJECT=0.5,KHOP_RCVD_UNTRUST=0.5,RCVD_IN_HOSTKARMA_W=-0.1,RCVD_IN_HOSTKARMA_WL=-2,URIBL_BLOCKED=0.001
--------------------------------------------------------------------------------
sa-summary includes the following options:
| Long | Short | Parameter | Description | Default value |
|---|---|---|---|---|
| --logdir | -l | directory | Directory containing spam logs | '.' |
| --file | -f | file/regex | File name or regular expression to look for in the logdir | '^maillog$' |
| --top | -t | integer | Number of top rules to display | 100 |
| --order | -o | string | Order of results in first 4 tables: RULE COUNT PERCMAIL PERCHAM PERCSPAM TOTSCO | COUNT/TOTSCO |
| --sa_threshold | -s | integer | Spamassassin score threshold | 5 |
| --bayes_threshold | -b | float | Bayes classifier threshold | 0.5 |
| --pos_threshold | -p | float | Positive spamassassin score threshold | 2 |
| --neg_threshold | -n | float | Negative spamassassin score threshold | -0.1 |
| --margin | -m | integer | Plus/minus margin for marginal classifications | 4 |
| --web | -w | N/A | Print web friendly output | N/A |
| --help | -h | N/A | Print help message | N/A |
If number of top rules to display is set to 0 then the following tables will not be displayed
- Rules hit:
- top ham rules
- top spam rules
- top positive ham rules
- top negative spam rules
If margin is set to 0 then the following tables will not be displayed
- Mail details:
- marginal mail classifications
If Bayes threshold is set to 0 then the following tables will not be displayed
- Mail details:
- ham with high Bayes scores
- spam with low Bayes scores
If both positive and negative spamassassin score thresholds are set to 0 then the following tables will not be displayed
- Mail details:
- ham with high positive rule scores
- spam with high positive rule scores
The following options will disable all tables except the initial summary tables:
./sa-summary -f spam-custom.log -p 0 -n 0 -m 0 -b 0 -t 0- Improve code
- Simplify some of the more complex functions
- Fix worst perlcritic issues
- Add unit tests
- Setup travis CI
- Possibly also setup kritika.io
- Improve documentation
- Add some of the README sections to the script in the form of pod markup
- Improve output
- Improve printf formatting
- Use %3.2f instead of %s with floats for example
- Improve error messages
- See Error messages chapter in the R tidyverse style guide
- Improve printf formatting
- Add functionality
- None planned
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
- sa-stats.pl by Dallas Engelken.
- The Apache Spamassassin sa-stats.pl script
- Spamassassin Statistics and log analyzers
- Spamassassin topic on github
Whatever license Dallas Engelken used.