August 2003 (It's about a year now since A Plan for Spam. So far, filters are winning. This article analyzes the tricks spammers have tried to beat them, and offers some suggestions for the future.) Bayesian filters are now common enough that we're starting to see spams designed specifically to get past them. So far these tricks aren't working. My filtering rate is still over 99.7%, and Brian Burton reports an astonishing 99.9% with his multi-word Bayesian SpamProbe. Will such filtering rates hold up? What are spammers doing to attack Bayesian filters? Is there anything we can do to make them tighter? More Good Tokens There are only two ways to get past a Bayesian filter: add more good tokens, or use fewer bad ones. Spammers are actively trying both. They try to add good tokens by inserting random dictionary words, or by attaching a big chunk of neutral text, typically from a book or a wire service article. Neither of these tricks works very well. Choosing words at random yields (as you might expect) words that are just as likely to occur in spams as in legitimate mail. The vocabulary of spams is a little narrower than that of legitimate mail, so spammers may get a slight benefit from adding random words, but it is mostly a wash, statistically. Most randomly chosen words turn out not to have occurred in either spam or nonspam mail, and therefore have neutral spam probabilities. (I still use .4 as the default for unseen words.) You can counter the noise of random tokens by using an occurrence threshold, as recommended in the Plan for Spam. I still use a threshold of 3. Appending chunks of articles or books doesn't seem to work any better, at least in the cases I've seen so far. The appended text doesn't look like spam, but it doesn't look much like the email I get either, so it tends not to have any effect, statistically. Many spammers now use randomly generated names in their From lines, but these turn out to make filtering easier: I get a lot of email from strangers, but none of them so far have been called Krystal or Louella. I think the names of most users' correspondents will fall into a small, consistent subset. So choosing names at random will yield tokens with high, not neutral, spam probabilities. Fewer Bad Tokens The other way to spoof a Bayesian filter is to use fewer bad tokens. There are two general strategies: try to conceal the bad tokens, or rewrite the email to use less spammy language. So far, trying to conceal bad tokens is a complete failure. All the tricks I've seen so far make the spams easier to catch, not harder. These include misspellings (V1agra), breaking up words with spaces or html (S E X), sending the spam as an image instead of text, and sending a Javascript program that generates the spam. Misspellings end up having higher spam probabilities than the words they're intended to conceal. In my filter the spam probability of "Viagra" is .9848, and of "V1agra" .9998. [1] For this kind of trick to work, you have to be the first person to use nearly every misspelling in a spam. The odds of doing that are low, and if you fail you merely teach the filter all the new misspellings. Breaking up words has the same effect. It is easy to make a tokenizer ignore most such tricks, but it probably wouldn't matter if you didn't bother. Legitimate email doesn't contain broken bits of words, so they quickly get high spam probabilities. In my filter the letter S by itself in the subject line has a spam probability of .9427. Sending the spam as an image instead of text doesn't work either, because you need certain html tags to display an image, and these all end up having very high spam probabilities. Particularly the url. If you use a domain name and it's one that has shown up in spams before, you're dead. If you use an ip address instead, you're even deader. No tokens have higher spam probabilities than numbers in a url. Sending the spam hidden within a Javascript program fails for a similar reason. Javascript is even rarer in legitimate mails than img tags, so the tokens in a Javascript program get very high spam probabilities. Rewriting Rewriting the spam in less spammy language is the only one of these strategies likely to succeed. But this takes a lot of work. It may not even be possible for some spams. How do you rewrite a mortgage spam without using terms like "refinance" (.9612), "lenders" (.9862), or "mortgage" (.9995)? And remember, whatever euphemisms you use, they have to be different from the ones used by every mortgage spammer before you. Surely at this point it would be less work for the spammer to switch to some more legitimate business. That's an important consideration. If the only way to get past Bayesian filters is to write spams more cleverly, we've made spamming a lot harder, because we've shifted the burden of cleverness from the few comparatively smart people who write spamware to the large number of stupider people who write the spams. The infrastructure of spam is built by smart people, what Jon Praed called "hackers gone bad." The spams themselves, however, are written by the individual spammers. Spamware can only help them so far. It can insert random words into the spam for them, or break up and misspell words, but it can't rewrite the spam in less spammy language. It would take AI to do that. When the spammers do try to rewrite their messages, they'll probably do it by replacing individual spammy tokens with phrases of more neutral words. But multi-word filters will learn and catch these phrases too. Brian Burton's Spamprobe and Bill Yerazunis' CRM114 already look at multi-word patterns. When a spam gets through my filter I send it on to them, and they always seem to be able to catch it. And of course, spams won't work so well if they have to be rewritten in more neutral language. People who respond to spams are presumably pretty dull-witted, and have to be hit over the head with a lot of capital letters and exclamation points to get them to do anything. Perhaps you can't get them to act at all unless you tell them they have to ACT NOW! So forcing spammers to use more neutral language may be enough to put most of them out of business. We'll see in the coming year. The Future This battle has only just started. I've only been seeing spams that seem intended specifically to spoof Bayesian filters for a couple months. But we'll be seeing a lot more now that AOL has released Bayesian filters. How will the battle play out? As I said in the Plan for Spam, I think it may all come down to links. The Web is the main cause of spam, not email. Nearly all spams include some kind of contact mechanism, and this is nearly always a link. [2] This is the part of the spam that filter writers should focus on, because this is the hardest for the spammer to change. Better Bayesian Filtering mentioned that filters could be made more discriminating by marking tokens with their context. "FREE" in the subject line has a much higher spam probability (.9999) than "FREE" in the body (.7567). So far I have only marked tokens with the name of the header line they occur in. This idea could be expanded to squeeze more information out of links. Tokens like "here", "remove", or "img" within a link will have a much higher spam probability than they would otherwise. (This would probably be true for tokens within e.g. font tags, too.) In response to filters, spams are getting smaller. In many the payload now consists of a single image tag within a link:
    <a href="http://sea2ws.com/gp/default.asp?id=011">
    <img src="../pharmhere1.com/pics/gp1.jpg"></a>
But this much html is still enough to catch the spam. In effect, you can recognize this kind of spam by its form. If these spams have anything in them besides the link and the image tag, it's usually just chaff-- random text intended to spoof filters. So far such chaff is ineffective, but we can't assume it will stay that way. There are ways to generate text that would work better at counterbalancing the spamminess of the link and the image. Eventually filters might have to take steps to recognize and ignore chaff. For example, if an email had some tokens with very high spam probabilities, and others with very low spam probabilities, you might want to ask if the spammy words all occurred close together, or rescore it looking only at the html. Another way to factor out chaff might be to look at whether the text seemed grammatical. You wouldn't necessarily have to parse it. For English, at least, I can imagine several ways to come up with a quick statistical estimate. The hardest kind of spams to catch are those I've called "spam of the future"-- a little plain text plus a url:
    Hey there. Check out the following:
    http://www.blackboxhosting.com/foo
The future has arrived. I regularly see spams like this now. I still catch nearly all of them-- headers alone would be enough to catch most current spam-- but the .3% of spam that I miss is mostly spam of the future. In spam of the future, the sales pitch is pushed one step back. Instead of being contained in the email itself, as in an ordinary spam, it is waiting a click away on a web site. This trend is encouraging, because it implies that filters are winning. Spam is literally retreating. (This is more than a symbolic victory; each extra step cuts response rates.) If the spam is waiting on a web site, why not have filters go look at what's there? You could apply the filtering algorithm pretty much unchanged to the contents of the site. Richard Jowsey of death2spam has already started doing this in borderline cases, and he reports that it works well. A cheaper alternative would be a cooperative list of sites advertised in spams. Instead of examining the site, a filter could query a server (or p2p network) to see if other users had recently reported spams promoting that domain. If so it could be treated as a token with very high spam probability. (A cooperative list of spamvertised sites would be useful for other purposes as well.) This idea could be generalized to a cooperative list of all tokens with high spam probabilities, not just domain names. This would improve filtering rates for everyone, but particularly for users of newly installed filters, which would now need little training. To take advantage of this kind of information, we should ideally delay filtering as long as possible. I.e. filter when the user checks his mail, not when it arrives at the server. By the time you check your mail, odds are that any spam that made it into in your inbox has already been seen by thousands of people. Divide and Conquer Some people believe that spammers will inevitably figure out a way around filters, and that a better solution is to have laws making spamming a federal crime. I'd love to see such laws myself, at least if they were written properly. And strangely enough, I think filters will help this to happen. There is a class of spammers who couldn't evade filters even if they knew how, and eliminating these will make it easier to attack the rest. It's hard to pass effective laws against spam now, because there is a continuum of spammers, ranging from (ahem) "permission-based email marketers" like Virtumundo that send unsolicited email to addresses they buy from sites with unscrupulous privacy policies, to bottom-feeders like Alan Ralsky who send unsolicited email to addresses culled from web pages, chat rooms, and newsgroups. [3] The companies at the more legitimate end of the spectrum lobby for loopholes that allow the bottom-feeders to slip through too. For example, congress seems to be considering allowing unsolicited mail so long as it contains a working unsubscribe link. [4] This despite the fact that most experts advise against clicking on unsubscribe links, because they just tell the more unscrupulous spammers that you're a live target. How does congress expect the email recipient to be able to figure out which unsubscribe links yield less spam, and which yield more? Filters will help fix this situation, by putting the "opt-in" spammers out of business. Such companies can't take serious measures to spoof filters (e.g. falsifying headers) and still maintain the fiction that the tens of millions of people on their lists are "subscribers" who actually want to receive their valuable offers. [5] The result is that "opt-in" spam is very easy to filter. I can't imagine any Bayesian filter, however badly implemented, not catching this stuff. I don't think I've ever had a single one get through mine. As the growing volume of spam encourages widespread use of filters, the number of people who see spam sent by the "opt-in" mailers will gradually shrink down to nothing. And with it the companies themselves. True, this would put the wrong half of the spammers out of business. But getting rid of the "opt-in" spammers will ultimately hurt the bottom-feeders too. If the "opt-in" spammers went away, leaving a clear gap between L. L. Bean and Alan Ralsky, it would be easier to pass laws that distinguished between them. It would be clear to everyone where marketing ended and crime began, and there would be no lobbyists working to blur the distinction. Putting a lock on your door may not keep everyone out, but it makes it easy to distinguish between the people you invite and the people who break in. Notes [1] The difference wouldn't be as great for most people, but much of my mail is about spam, and so often contains the word "Viagra". [2] About 95% of spam sent to me contains urls. The rest are evenly divided between 419, MLM, and non-ascii (mostly Russian) spams, most of which want you to respond by email, and all of which are easy to filter. Anyone want to write an ELIZA to talk to these people? [3] Is there that much difference between these two cases? [4] "Unsubscribe" is of course a misnomer. You didn't subscribe to their email list. They just bought your address from someone. But for better or worse getting yourself off a spammer's list has come to be called unsubscribing. [5] I did once get a spam promoting Omaha Steaks in which the subject line read
    12_F.R.E.E._Hamburgers & Half_Price_Steaks
This is pushing the limits of plausible deniability. You have to wonder how they can continue to claim they're sending offers to willing recipients, while at the same time taking such obvious steps to spoof filters. What's next? Mail from 0ma_ha 5teakz?