Branden and I had an idea to help with the spam problem on our system, and it’s proven particularly effective. How effective? Here’s the graphs from the last year of email on my system. Can you tell when I started using the system?
If you want to see the live images, check here.
The idea is based on the following observations: certain addresses on my domain ONLY get spam. This is generally because they either don’t exist or because I stopped using them; for example, spammers often send email to buy@memoryhole.net. Branden and I also both use the user-tag@domain scheme, so we get a lot of disposable addresses that way. These addresses are such that we know for certain that anyone sending email to them is a spammer. Some of these addresses were already being rejected as invalid; some we hadn’t gotten around to invalidating yet.
By simply rejecting emails sent to those addresses, we were able to reduce the spam load of our domains by a fair bit, and the false-positive rate is nil. But we took things a step further: since spammers rarely send only one message, often they will send spam to both invalid AND valid addresses.
If I view those known-bad addresses as, essentially, honeypots, I can say: aha! Any IP sending to a known-bad address is a spammer, and I can refuse (with a permanent fail) any email from that IP for some short time. I started with 5 minutes, but have moved to an exponentially increasing timeout system. Each additional spam increased the length of the timeout (5 minutes for the first spam, 6 for the second, 8 for the third, and so on). Longer-term bans, as a result of the exponentially increasing timeout, are made more efficient via the equivalent of /etc/hosts.deny. I haven’t gotten into the maintaining-my-spammer-database much yet, but I think this may not be terribly important (I’ll explain in a moment).
One of the best parts of the system is that it is fast: new spammers that identify themselves by sending to honeypot addresses get blocked quickly and without my intervention. So far this has been particularly helpful in eliminating spam spikes. Another feature that I originally thought would be useful, but hasn’t really appeared to be (yet) is that it allows our multiple domains to share information about spam sources. Thus far, however, our domains seem to be plagued by different spammers.
Now, interestingly, about a week after we started using the system, our database of known spammers was wiped out (it’s kept in /tmp, and we rebooted the system). Result? No noticeable change in effectiveness. How’s that for a result? And, as you can see from the graph above, there’s no obvious change in spam blocking over the course of a month that would indicate that the long-term history is particularly useful. So, it may be sufficient to keep a much shorter history. Maybe only a week is necessary, maybe two weeks, I haven’t decided yet (and, as there hasn’t yet been much of a speed penalty for it, there’s no pressure to establish a cutoff). But, given that most spam is sent from botnets with dynamic IPs, this isn’t a particularly surprising behavior.
Forkit.org and memoryhole.net have been using this filter for a month so far. The week before we started using this filter, memoryhole.net averaged around 262 emails per hour. The week after instituting this filter, the average was around 96 per hour (a 60+% reduction!). Before using the filter, forkit.org averaged 70 emails per hour; since starting to use the filter, that number is down to 27.4 per hour (also a 60+% reduction). We have recorded spams from over 33,000 IPs, most of which only ever sent one or two spams. We typically have between 100 and 150 IPs that are “in jail” at any one time (at this moment: 143), and most of those (at this moment 134) are blocked for sending more than ten spams (114 of them have a timeout measured in days rather than minutes).
Now, granted, I know that by simply dropping 60% of all connections we’d get approximately the same results. But I think our particular technique is superior to that because it’s based on known-bad addresses. Anyone who doesn’t send to invalid addresses will never notice the filter.
The biggest potential problem that I can see with this system is that of spammers who have taken over a normally friendly host, such as Gmail spam. I’ve waffled on this potential problem: on the one hand, Gmail has so many outbound servers that it’s unlikely to get caught (a couple bad emails won’t have much of a penalty). Thus far, I’ve seen a few yahoo servers in Japan sending us spam, but no Gmail servers. On the other hand, as long as I simply use temporary failures (at least for good addresses), and as long as ND doesn’t retry in the same order every time, messages will get through.
I’ve also begun testing a “restricted sender” feature to work with this. For example, I have the address kyle-slashdot@memoryhole.net that I use exclusively for my slashdot.org account. The only people who are allowed to send to that email address is slashdot.org (i.e. if I forget my password). If anyone from any other domain attempts that address, well, then I know that sending IP is a spammer and I can treat it as if it was a known-bad address. Not applicable to every email address, obviously, but it’s a start.
It’s been pointed out that this system is, in some respects, a variant on greylisting. The major difference is that it’s a penalty-based system, rather than a “prove yourself worthy by following the RFC” system, and I like that a bit better. I’m somewhat tempted to define some bogus address (bogus@memoryhole.net) and sign it up for spam (via spamyourenemies.com or something similar), but given that part of the benefit here is due to spammers trying both valid and invalid addresses, I think it would probably just generate lots of extra traffic and not achieve anything particularly useful.
Now, this technique is simply one of many; it’s not sufficient to guarantee a spam-free inbox. I use it in combination with several other antispam techniques, including a greet-delay system and a frequently updated SpamAssassin setup. But check out the difference it’s made in our CPU utilization:
Okay, so, grand scheme of things: knocking the CPU use down three percentage points isn’t huge, but knocking it down by 50%? That sounds better, anyway. And as long as it doesn’t cause problems by making valid email disappear (possible, but rather unlikely), it seems to me to be a great way to cut my spam load relatively easily.
