I know that people rave about GMail's spam filtering, but it didn't work for me: I was seeing too many false positives. I personally prefer to see some false negatives (i.e. letting some spam through), but to reduce false positives as much as possible (and ideally have a way to tune this).

Here's the local SpamAssassin setup I have put together over many years. In addition to the parts I describe here, I also turn off greylisting on my email provider (KolabNow) because I don't want to have to wait for up to 10 minutes for a "2FA" email to go through.

This setup assumes that you download all of your emails to your local machine. I use fetchmail for this, though similar tools should work too.

Three tiers of emails

The main reason my setup works for me, despite my receiving hundreds of spam messages every day, is that I split incoming emails into three tiers via procmail:

1. not spam: delivered to inbox
2. likely spam: quarantined in a soft_spam/ folder
3. definitely spam: silently deleted

I only ever have to review the likely spam tier for false positives, which is on the order of 10-30 spam emails a day. I never even see the the hundreds that are silently deleted due to a very high score.

This is implemented based on a threshold in my .procmailrc:

# Use spamassassin to check for spam
:0fw: .spamassassin.lock
| /usr/bin/spamassassin

# Throw away messages with a score of > 12.0
:0
* ^X-Spam-Level: \*\*\*\*\*\*\*\*\*\*\*\*
/dev/null

:0:
* ^X-Spam-Status: Yes
$HOME/Mail/soft_spam/

# Deliver all other messages
:0:
${DEFAULT}

I also use the following ~/.muttrc configuration to easily report false negatives/positives and examine my likely spam folder via a shortcut in mutt:

unignore X-Spam-Level
unignore X-Spam-Status

macro index S "c=soft_spam/\n" "Switch to soft_spam"

# Tell mutt about SpamAssassin headers so that I can sort by spam score
spam "X-Spam-Status: (Yes|No), (hits|score)=(-?[0-9]+\.[0-9])" "%3"
folder-hook =soft_spam 'push ol'
folder-hook =spam 'push ou'

# <Esc>d = de-register as non-spam, register as spam, move to spam folder.
macro index \ed "<enter-command>unset wait_key\n<pipe-entry>spamassassin -r\n<enter-command>set wait_key\n<save-message>=spam\n" "report the message as spam"

# <Esc>u = unregister as spam, register as non-spam, move to inbox folder.
macro index \eu "<enter-command>unset wait_key\n<pipe-entry>spamassassin -k\n<enter-command>set wait_key\n<save-message>=inbox\n" "correct the false positive (this is not spam)"

Custom SpamAssassin rules

In addition to the default ruleset that comes with SpamAssassin, I've also accrued a number of custom rules over the years.

The first set comes from the (now defunct) SpamAssassin Rules Emporium. The second set is the one that backs bugs.debian.org and lists.debian.org. Note this second one includes archived copies of some of the SARE rules and so I only use some of the rules in the common/ directory.

Finally, I wrote a few custom rules of my own based on specific kinds of emails I have seen slip through the cracks. I haven't written any of those in a long time and I suspect some of my rules are now obsolete. You may want to do your own testing before you copy these outright.

In addition to rules to match more spam, I've also written a ruleset to remove false positives in French emails coming from many of the above custom rules. I also wrote a rule to get a bonus to any email that comes with a patch:

describe FM_PATCH   Includes a patch
body FM_PATCH   /\bdiff -pruN\b/
score FM_PATCH  -1.0

since it's not very common in spam emails

SpamAssassin settings

When it comes to my system-wide SpamAssassin configuration in /etc/spamassassin/, I enable the following plugins:

loadplugin Mail::SpamAssassin::Plugin::AntiVirus
loadplugin Mail::SpamAssassin::Plugin::AskDNS
loadplugin Mail::SpamAssassin::Plugin::ASN
loadplugin Mail::SpamAssassin::Plugin::AutoLearnThreshold
loadplugin Mail::SpamAssassin::Plugin::Bayes
loadplugin Mail::SpamAssassin::Plugin::BodyEval
loadplugin Mail::SpamAssassin::Plugin::Check
loadplugin Mail::SpamAssassin::Plugin::DKIM
loadplugin Mail::SpamAssassin::Plugin::DNSEval
loadplugin Mail::SpamAssassin::Plugin::FreeMail
loadplugin Mail::SpamAssassin::Plugin::FromNameSpoof
loadplugin Mail::SpamAssassin::Plugin::HashBL
loadplugin Mail::SpamAssassin::Plugin::HeaderEval
loadplugin Mail::SpamAssassin::Plugin::HTMLEval
loadplugin Mail::SpamAssassin::Plugin::HTTPSMismatch
loadplugin Mail::SpamAssassin::Plugin::ImageInfo
loadplugin Mail::SpamAssassin::Plugin::MIMEEval
loadplugin Mail::SpamAssassin::Plugin::MIMEHeader
loadplugin Mail::SpamAssassin::Plugin::OLEVBMacro
loadplugin Mail::SpamAssassin::Plugin::PDFInfo
loadplugin Mail::SpamAssassin::Plugin::Phishing
loadplugin Mail::SpamAssassin::Plugin::Pyzor
loadplugin Mail::SpamAssassin::Plugin::Razor2
loadplugin Mail::SpamAssassin::Plugin::RelayEval
loadplugin Mail::SpamAssassin::Plugin::ReplaceTags
loadplugin Mail::SpamAssassin::Plugin::Rule2XSBody
loadplugin Mail::SpamAssassin::Plugin::SpamCop
loadplugin Mail::SpamAssassin::Plugin::TextCat
loadplugin Mail::SpamAssassin::Plugin::TxRep
loadplugin Mail::SpamAssassin::Plugin::URIDetail
loadplugin Mail::SpamAssassin::Plugin::URIEval
loadplugin Mail::SpamAssassin::Plugin::VBounce
loadplugin Mail::SpamAssassin::Plugin::WelcomeListSubject
loadplugin Mail::SpamAssassin::Plugin::WLBLEval

Some of these require extra helper packages or Perl libraries to be installed. See the comments in the relevant *.pre files or use this command to install everything:

apt install spamassassin  pyzor razor libencode-detect-perl liblog-log4perl-perl libgeoip-dev libmail-dkim-perl libarchive-zip-perl libio-string-perl libmail-dmarc-perl fuzzyocr

My ~/.spamassassin/user_prefs file contains the following configuration:

required_hits   5
ok_locales en fr

# Bayes options
score BAYES_00 -4.0
score BAYES_40 -0.5
score BAYES_60 1.0
score BAYES_80 2.7
score BAYES_95 4.0
score BAYES_99 6.0
bayes_auto_learn 1
bayes_ignore_header X-Miltered
bayes_ignore_header X-MIME-Autoconverted
bayes_ignore_header X-Evolution
bayes_ignore_header X-Virus-Scanned
bayes_ignore_header X-Forwarded-For
bayes_ignore_header X-Forwarded-By
bayes_ignore_header X-Scanned-By
bayes_ignore_header X-Spam-Level
bayes_ignore_header X-Spam-Status

as well as manual score reductions due to false positives, and manual score increases to help push certain types of spam emails over the 12.0 definitely spam threshold.

Finally, I have the FuzzyOCR package installed since it has occasionally flagged some spam that other tools had missed. It is a little resource intensive though and so you may want to avoid this one if you are filtering spam for other people.

As always, feel free to leave a comment if you do something else that works well and that's not included in my setup. This is a work-in-progress.