More on Spam Filtering

I tweaked the policyd rules and my a bit more, so that my mailservers lets PolicyD do most of the examinations. The net effect was that Postfix itself (my mailserver, or MTA to be more accurate) stopped rejecting as many hosts, instead allowing PolicyD, a plugin I use to do some more advanced filtering, to handle those hosts. And this is a good thing because Postfix would just reject the message, whereas PolicyD adds the host to a blacklist first.

I noticed an interesting change as a result, though: at any given time, fewer hosts were sitting in my greylist table, and more hosts were sitting in my blacklist. As I type this, there are 60 hosts in my greylisting table, and 985 hosts in my blacklist table. (This isn’t a totally fair comparison, as the blacklist keeps hosts for 14 days, while the greylist table keeps hosts for 3 days.)

I significantly revamped the page listing the banned hosts, both to cache the output (since each of the hundred hosts now involves running 6 DNS lookups and parsing two multi-meg text files), and to list a lot more output. I don’t currently use any DNSBLs (DNS blacklists), but set the page up to show whether a given host matches those blacklists.

At the time of this writing, 96 of the 100 most recent hosts have been in the Spamhaus XBL, which lists “hijacked PCs infected by illegal 3rd party exploits.” 96%! Spamcop is the next best blacklist, with 76% matching, followed by 64% matching results from the Spamhaus PBL, which lists IPs that are for “end-users,” like residential cable modems and dial-up lines: things mailservers shouldn’t be on. (If you’re like me, BTW, the answer is, “Yes, you can remove yourself” if you run a legitimate mailserver on those netblocks.) I’ve also had good results with the NiX Spam blacklist, which I found mentioned on the page for OpenBSD’s spamd.

I’m pretty strongly against using blacklists for anything definitive, as they’ve been historically fraught with problems and abuses, with many administrators eager to list whole netblocks, or even people they don’t like. And my simple setup seems to be just shy of 100% effective in stopping spam, so I have no incentive to go for blacklists anyway. Plus, this is an analysis of how spam shows up in blacklists, but that doesn’t tell us anything about how many legitimate e-mails show up in those blacklists, which is an equally important metric to consider.

But if I were to use DNSBLs, I’d give strong consideration to the following:

  •, which blends the XBL and PBL, plus a general list of spammers.
  •, which has myriad blacklists; pay special attention to “web” ( and the dynamic IP list (
  •, which is quite well-known.

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