Plan for Spam, Version 2 464
bugbear writes "I just posted a new version of the Plan for
Spam Bayesian filtering algorithm. The big change is to mark tokens by context. The new version decreases spams missed by 50%, to 2.5 per 1000, even though spam has gotten harder to filter since the summer. I also talk about how spam will evolve, and what to do about it."
GREAT! (Score:2)
Please carry on with this Bayesian Spam filtering! It'll be the death of spam yet!
popfile URL (Score:5, Informative)
The url for the project is popfile.sourceforge.net [sourceforge.net]
I didn't try it yet, but it I will try it really soon now!
Another POP Proxy program, SpamPal (Score:3, Informative)
I'm sorry, but someone has to say it... (Score:2, Funny)
Re:I'm sorry, but someone has to say it... (Score:2)
Archive Version (b/c it's a personal site) (Score:5, Interesting)
(This article was given as a talk at the 2003 Spam Conference. It describes the work I've done to improve the performance of the algorithm described in A Plan for Spam, and what I plan to do in the future.)
The first discovery I'd like to present here is an algorithm for lazy evaluation of research papers. Just write whatever you want and don't cite any previous work, and indignant readers will send you references to all the papers you should have cited. I discovered this algorithm after ``A Plan for Spam'' [1] was on Slashdot.
Spam filtering is a subset of text classification, which is a well established field, but the first papers about Bayesian spam filtering per se seem to have been two given at the same conference in 1998, one by Pantel and Lin [2], and another by a group from Microsoft Research [3].
When I heard about this work I was a bit surprised. If people had been onto Bayesian filtering four years ago, why wasn't everyone using it? When I read the papers I found out why. Pantel and Lin's filter was the more effective of the two, but it only caught 92% of spam, with 1.16% false positives.
When I tried writing a Bayesian spam filter, it caught 99.5% of spam with less than
So why did we get such different numbers? I haven't tried to reproduce Pantel and Lin's results, but from reading the paper I see five things that probably account for the difference.
One is simply that they trained their filter on very little data: 160 spam and 466 nonspam mails. Filter performance should still be climbing with data sets that small. So their numbers may not even be an accurate measure of the performance of their algorithm, let alone of Bayesian spam filtering in general.
But I think the most important difference is probably that they ignored message headers. To anyone who has worked on spam filters, this will seem a perverse decision. And yet in the very first filters I tried writing, I ignored the headers too. Why? Because I wanted to keep the problem neat. I didn't know much about mail headers then, and they seemed to me full of random stuff. There is a lesson here for filter writers: don't ignore data. You'd think this lesson would be too obvious to mention, but I've had to learn it several times.
Third, Pantel and Lin stemmed the tokens, meaning they reduced e.g. both ``mailing'' and ``mailed'' to the root ``mail''. They may have felt they were forced to do this by the small size of their corpus, but if so this is a kind of premature optimization.
Fourth, they calculated probabilities differently. They used all the tokens, whereas I only use the 15 most significant. If you use all the tokens you'll tend to miss longer spams, the type where someone tells you their life story up to the point where they got rich from some multilevel marketing scheme. And such an algorithm would be easy for spammers to spoof: just add a big chunk of random text to counterbalance the spam terms.
Finally, they didn't bias against false positives. I think any spam filtering algorithm ought to have a convenient knob you can twist to decrease the false positive rate at the expense of the filtering rate. I do this by counting the occurrences of tokens in the nonspam corpus double.
I don't think it's a good idea to treat spam filtering as a straight text classification problem. You can use text classification techniques, but solutions can and should reflect the fact that the text is email, and spam in particular. Email is not just text; it has structure. Spam filtering is not just classification, because false positives are so much worse than false negatives that you should treat them as a different kind of error. And the source of error is not just random variation, but a live human spammer working actively to defeat your filter.
Tokens
Another project I heard about after the Slashdot article was Bill Yerazunis' CRM114 [5]. This is the counterexample to the design principle I just mentioned. It's a straight text classifier, but such a stunningly effective one that it manages to filter spam almost perfectly without even knowing that's what it's doing.
Once I understood how CRM114 worked, it seemed inevitable that I would eventually have to move from filtering based on single words to an approach like this. But first, I thought, I'll see how far I can get with single words. And the answer is, surprisingly far.
Mostly I've been working on smarter tokenization. On current spam, I've been able to achieve filtering rates that approach CRM114's. These techniques are mostly orthogonal to Bill's; an optimal solution might incorporate both.
``A Plan for Spam'' uses a very simple definition of a token. Letters, digits, dashes, apostrophes, and dollar signs are constituent characters, and everything else is a token separator. I also ignored case. Now I have a more complicated definition of a token:
Case is preserved.
Exclamation points are constituent characters.
Periods and commas are constituents if they occur between two digits. This lets me get ip addresses and prices intact.
A price range like $20-25 yields two tokens, $20 and $25.
Tokens that occur within the To, From, Subject, and Return-Path lines, or within urls, get marked accordingly. E.g. ``foo'' in the Subject line becomes ``Subject*foo''. (The asterisk could be any character you don't allow as a constituent.)
Such measures increase the filter's vocabulary, which makes it more discriminating. For example, in the current filter, ``free'' in the Subject line has a spam probability of 98%, whereas the same token in the body has a spam probability of only 65%.
In the Plan for Spam filter, all these tokens would have had the same probability,
The disadvantage of having a larger universe of tokens is that there is more chance of misses. Spreading your corpus out over more tokens has the same effect as making it smaller. If you consider exclamation points as constituents, for example, then you could end up not having a spam probability for free with seven exclamation points, even though you know that free with just two exclamation points has a probability of 99.99%.
One solution to this is what I call degeneration. If you can't find an exact match for a token, treat it as if it were a less specific version. I consider terminal exclamation points, uppercase letters, and occurring in one of the five marked contexts as making a token more specific. For example, if I don't find a probability for ``Subject*free!'', I look for probabilities for ``Subject*free'', ``free!'', and ``free'', and take whichever one is farthest from
Here are the alternatives [7] considered if the filter sees ``FREE!!!'' in the Subject line and doesn't have a probability for it.
If you do this, be sure to consider versions with initial caps as well as all uppercase and all lowercase. Spams tend to have more sentences in imperative voice, and in those the first word is a verb. So verbs with initial caps have higher spam probabilities than they would in all lowercase. In my filter, the spam probability of ``Act'' is 98% and for ``act'' only 62%.
If you increase your filter's vocabulary, you can end up counting the same word multiple times, according to your old definition of ``same''. Logically, they're not the same token anymore. But if this still bothers you, let me add from experience that the words you seem to be counting multiple times tend to be exactly the ones you'd want to.
Another effect of a larger vocabulary is that when you look at an incoming mail you find more interesting tokens, meaning those with probabilities far from
For example, the token ``dalco'' occurs 3 times in my spam corpus and never in my legitimate corpus. The token ``Url*optmails'' (meaning ``optmails'' within a url) occurs 1223 times. And yet, as I used to calculate probabilities for tokens, both would have the same spam probability, the threshold of
That doesn't feel right. There are theoretical arguments for giving these two tokens substantially different probabilities (Pantel and Lin do), but I haven't tried that yet. It does seem at least that if we find more than 15 tokens that only occur in one corpus or the other, we ought to give priority to the ones that occur a lot. So now there are two threshold values. For tokens that occur only in the spam corpus, the probability is
I may later scale token probabilities substantially, but this tiny amount of scaling at least ensures that tokens get sorted the right way.
Another possibility would be to consider not just 15 tokens, but all the tokens over a certain threshold of interestingness. Steven Hauser does this in his statistical spam filter [8]. If you use a threshold, make it very high, or spammers could spoof you by packing messages with more innocent words.
Finally, what should one do about html? I've tried the whole spectrum of options, from ignoring it to parsing it all. Ignoring html is a bad idea, because it's full of useful spam signs. But if you parse it all, your filter might degenerate into a mere html recognizer. The most effective approach seems to be the middle course, to notice some tokens but not others. I look at a, img, and font tags, and ignore the rest. Links and images you should certainly look at, because they contain urls.
I could probably be smarter about dealing with html, but I don't think it's worth putting a lot of time into this. Spams full of html are easy to filter. The smarter spammers already avoid it. So performance in the future should not depend much on how you deal with html.
Performance
Between December 10 2002 and January 10 2003 I got about 1750 spams. Of these, 4 got through. That's a filtering rate of about 99.75%.
Two of the four spams I missed got through because they happened to use words that occur often in my legitimate email.
The third was one of those that exploit an insecure cgi script to send mail to third parties. They're hard to filter based just on the content because the headers are innocent and they're careful about the words they use. Even so I can usually catch them. This one squeaked by with a probability of
Of course, looking at multiple token sequences would catch it easily. ``Below is the result of your feedback form'' is an instant giveaway.
The fourth spam was what I call a spam-of-the-future, because this is what I expect spam to evolve into: some completely neutral text followed by a url. In this case it was was from someone saying they had finally finished their homepage and would I go look at it. (The page was of course an ad for a porn site.)
If the spammers are careful about the headers and use a fresh url, there is nothing in spam-of-the-future for filters to notice. We can of course counter by sending a crawler to look at the page. But that might not be necessary. The response rate for spam-of-the-future must be low, or everyone would be doing it. If it's low enough, it won't pay for spammers to send it, and we won't have to work too hard on filtering it.
Now for the really shocking news: during that same one-month period I got three false positives.
In a way it's a relief to get some false positives. When I wrote ``A Plan for Spam'' I hadn't had any, and I didn't know what they'd be like. Now that I've had a few, I'm relieved to find they're not as bad as I feared. False positives yielded by statistical filters turn out to be mails that sound a lot like spam, and these tend to be the ones you would least mind missing [9].
Two of the false positives were newsletters from companies I've bought things from. I never asked to receive them, so arguably they were spams, but I count them as false positives because I hadn't been deleting them as spams before. The reason the filters caught them was that both companies in January switched to commercial email senders instead of sending the mails from their own servers, and both the headers and the bodies became much spammier.
The third false positive was a bad one, though. It was from someone in Egypt and written in all uppercase. This was a direct result of making tokens case sensitive; the Plan for Spam filter wouldn't have caught it.
It's hard to say what the overall false positive rate is, because we're up in the noise, statistically. Anyone who has worked on filters (at least, effective filters) will be aware of this problem. With some emails it's hard to say whether they're spam or not, and these are the ones you end up looking at when you get filters really tight. For example, so far the filter has caught two emails that were sent to my address because of a typo, and one sent to me in the belief that I was someone else. Arguably, these are neither my spam nor my nonspam mail.
Another false positive was from a vice president at Virtumundo. I wrote to them pretending to be a customer, and since the reply came back through Virtumundo's mail servers it had the most incriminating headers imaginable. Arguably this isn't a real false positive either, but a sort of Heisenberg uncertainty effect: I only got it because I was writing about spam filtering.
Not counting these, I've had a total of five false positives so far, out of about 7740 legitimate emails, a rate of
I don't think this number can be trusted, partly because the sample is so small, and partly because I think I can fix the filter not to catch some of these.
False positives seem to me a different kind of error from false negatives. Filtering rate is a measure of performance. False positives I consider more like bugs. I approach improving the filtering rate as optimization, and decreasing false positives as debugging.
So these five false positives are my bug list. For example, the mail from Egypt got nailed because the uppercase text made it look to the filter like a Nigerian spam. This really is kind of a bug. As with html, the email being all uppercase is really conceptually one feature, not one for each word. I need to handle case in a more sophisticated way.
So what to make of this
Future
What next? Filtering is an optimization problem, and the key to optimization is profiling. Don't try to guess where your code is slow, because you'll guess wrong. Look at where your code is slow, and fix that. In filtering, this translates to: look at the spams you miss, and figure out what you could have done to catch them.
For example, spammers are now working aggressively to evade filters, and one of the things they're doing is breaking up and misspelling words to prevent filters from recognizing them. But working on this is not my first priority, because I still have no trouble catching these spams [10].
There are two kinds of spams I currently do have trouble with. One is the type that pretends to be an email from a woman inviting you to go chat with her or see her profile on a dating site. These get through because they're the one type of sales pitch you can make without using sales talk. They use the same vocabulary as ordinary email.
The other kind of spams I have trouble filtering are those from companies in e.g. Bulgaria offering contract programming services. These get through because I'm a programmer too, and the spams are full of the same words as my real mail.
I'll probably focus on the personal ad type first. I think if I look closer I'll be able to find statistical differences between these and my real mail. The style of writing is certainly different, though it may take multiword filtering to catch that. Also, I notice they tend to repeat the url, and someone including a url in a legitimate mail wouldn't do that [11].
The outsourcing type are going to be hard to catch. Even if you sent a crawler to the site, you wouldn't find a smoking statistical gun. Maybe the only answer is a central list of domains advertised in spams [12]. But there can't be that many of this type of mail. If the only spams left were unsolicited offers of contract programming services from Bulgaria, we could all probably move on to working on something else.
Will statistical filtering actually get us to that point? I don't know. Right now, for me personally, spam is not a problem. But spammers haven't yet made a serious effort to spoof statistical filters. What will happen when they do?
I'm not optimistic about filters that work at the network level [13]. When there is a static obstacle worth getting past, spammers are pretty efficient at getting past it. There is already a company called Assurance Systems that will run your mail through Spamassassin and tell you whether it will get filtered out.
Network-level filters won't be completely useless. They may be enough to kill all the "opt-in" spam, meaning spam from companies like Virtumundo and Equalamail who claim that they're really running opt-in lists. You can filter those based just on the headers, no matter what they say in the body. But anyone willing to falsify headers or use open relays, presumably including most porn spammers, should be able to get some message past network-level filters if they want to. (By no means the message they'd like to send though, which is something.)
The kind of filters I'm optimistic about are ones that calculate probabilities based on each individual user's mail. These can be much more effective, not only in avoiding false positives, but in filtering too: for example, finding the recipient's email address base-64 encoded anywhere in a message is a very good spam indicator.
But the real advantage of individual filters is that they'll all be different. If everyone's filters have different probabilities, it will make the spammers' optimization loop, what programmers would call their edit-compile-test cycle, appallingly slow. Instead of just tweaking a spam till it gets through a copy of some filter they have on their desktop, they'll have to do a test mailing for each tweak. It would be like programming in a language without an interactive toplevel, and I wouldn't wish that on anyone.
Notes
[1] Paul Graham. ``A Plan for Spam.'' August 2002. http://paulgraham.com/spam.html.
Probabilities in this algorithm are calculated using a degenerate case of Bayes' Rule. There are two simplifying assumptions: that the probabilities of features (i.e. words) are independent, and that we know nothing about the prior probability of an email being spam.
The first assumption is widespread in text classification. Algorithms that use it are called ``naive Bayesian.''
The second assumption I made because the proportion of spam in my incoming mail fluctuated so much from day to day (indeed, from hour to hour) that the overall prior ratio seemed worthless as a predictor. If you assume that P(spam) and P(nonspam) are both
If you were doing Bayesian filtering in a situation where the ratio of spam to nonspam was consistently very high or (especially) very low, you could probably improve filter performance by incorporating prior probabilities. To do this right you'd have to track ratios by time of day, because spam and legitimate mail volume both have distinct daily patterns.
[2] Patrick Pantel and Dekang Lin. ``SpamCop-- A Spam Classification & Organization Program.'' Proceedings of AAAI-98 Workshop on Learning for Text Categorization.
[3] Mehran Sahami, Susan Dumais, David Heckerman and Eric Horvitz. ``A Bayesian Approach to Filtering Junk E-Mail.'' Proceedings of AAAI-98 Workshop on Learning for Text Categorization.
[4] At the time I had zero false positives out of about 4,000 legitimate emails. If the next legitimate email was a false positive, this would give us
[5] Bill Yerazunis. ``Sparse Binary Polynomial Hash Message Filtering and The CRM114 Discriminator.'' Proceedings of 2003 Spam Conference.
[6] In ``A Plan for Spam'' I used thresholds of
[7] There is a flaw here I should probably fix. Currently, when ``Subject*foo'' degenerates to just ``foo'', what that means is you're getting the stats for occurrences of ``foo'' in the body or header lines other than those I mark. What I should do is keep track of statistics for ``foo'' overall as well as specific versions, and degenerate from ``Subject*foo'' not to ``foo'' but to ``Anywhere*foo''. Ditto for case: I should degenerate from uppercase to any-case, not lowercase.
It would probably be a win to do this with prices too, e.g. to degenerate from ``$129.99'' to ``$--9.99'', ``$--.99'', and ``$--''.
You could also degenerate from words to their stems, but this would probably only improve filtering rates early on when you had small corpora.
[8] Steven Hauser. ``Statistical Spam Filter Works for Me.'' http://www.sofbot.com.
[9] False positives are not all equal, and we should remember this when comparing techniques for stopping spam. Whereas many of the false positives caused by filters will be near-spams that you wouldn't mind missing, false positives caused by blacklists, for example, will be just mail from people who chose the wrong ISP. In both cases you catch mail that's near spam, but for blacklists nearness is physical, and for filters it's textual.
In fairness, it should be added that the new generation of responsible blacklists, like the SBL, cause far fewer false positives than earlier blacklists like the MAPS RBL, for whom causing large numbers of false positives was a deliberate technique to get the attention of ISPs.
[10] If spammers get good enough at obscuring tokens for this to be a problem, we can respond by simply removing whitespace, periods, commas, etc. and using a dictionary to pick the words out of the resulting sequence. And of course finding words this way that weren't visible in the original text would in itself be evidence of spam.
Picking out the words won't be trivial. It will require more than just reconstructing word boundaries; spammers both add (``xHot nPorn cSite'') and omit (``P#rn'') letters. Vision research may be useful here, since human vision is the limit that such tricks will approach.
[11] In general, spams are more repetitive than regular email. They want to pound that message home. I currently don't allow duplicates in the top 15 tokens, because you could get a false positive if the sender happens to use some bad word multiple times. (In my current filter, ``dick'' has a spam probabilty of
[12] This is what approaches like Brightmail's will degenerate into once spammers are pushed into using mad-lib techniques to generate everything else in the message.
[13] It's sometimes argued that we should be working on filtering at the network level, because it is more efficient. What people usually mean when they say this is: we currently filter at the network level, and we don't want to start over from scratch. But you can't dictate the problem to fit your solution.
Historically, scarce-resource arguments have been the losing side in debates about software design. People only tend to use them to justify choices (inaction in particular) made for other reasons.
Thanks to Sarah Harlin, Trevor Blackwell, and Dan Giffin for reading drafts of this paper, and to Dan again for most of the infrastructure that this filter runs on.
Problem you say? (Score:3, Funny)
would do the trick.
Stop spam? (Score:5, Interesting)
Anyway, I've said a few times the only way to effectively stop spam is to make it more expensive to the companies having it done. Filtering, blocking ports, refusing mail from RBL'd hosts all helps, but it will not stop until it is fully against the law and people bring legal action to stop it.
Even people who are supposed to be clueful don't get it. I got spammed to buy EZ-Pass for the PA Turnpike. I sent a nastygram to the state DoT. The keyboard monkey responded that I should look closely at the email, that I signed up to receive it. If I had a dollar for every site that claimed I signed up with them I would be rich. What an idiot.
Re:Stop spam? (Score:2, Informative)
Re:Stop spam? (Score:2)
His response was that I must have signed up for it as the email said so, and we all know that everything on the Internet is true. ;)
Re:Stop spam? (Score:2)
I have a few e-mail addresses that have only shown up on sites like Slashdot, or in a newsgroup. These invariably get the most spam, and most messages contain that "You signed up at one of our partner sites" disclaimer swearing that what I'm reading isn't spam. Whatever. They're hoping that the confusion this causes is sufficient to cast doubt on your complaint, since it's hard to prove that you were or were not "subscribed" by going to some anonymous 3rd party site and providing them with your details.
Re:Stop spam? (Score:2, Insightful)
Re:Stop spam? (Score:5, Insightful)
Actually spamassassin has a nice built-in reporting toolAnd if you setup it up to work with with Vipul's Razor [sourceforge.net] for it's all automagically updated.
Re:Stop spam? (Score:5, Insightful)
You say that it will stop if it's fully against the law and people bring legal action to stop it.
Last time I checked, murder was illegal, punishable by death in many states, yet it still occurs.
Re:Stop spam? (Score:4, Insightful)
People spam because it is rational to do so (or at least spammers make them think so). Very low costs, the possibility of a good return, and nothing to lose since there are virtually no spam laws.
A better comparison than murder is the practice of child labor. While it was legal it was a rational practice to engage in, because the return was high and the risk was low -- if a kid gets eaten by a machine you just find another kid. Now that is illegal the practice is almost completely extinct because it is no longer rational -- the police would come knocking at the door, which impedes the goal of running a profitable business.
Re:Stop spam? (Score:3, Funny)
. .
Last time I checked, murder was illegal, punishable by death in many states, yet it still occurs.
Spam is a means to an end - selling your shit to gullible people. Murder is not just a means, but an end in itself. When you want someone dead, there's not really another way around it. With spam, there's always telemarketing and pop-ups.
In addition, murder can be a crime of passion, while spamming is hardly such. I can't remember ever thinking "Oh that bastard cut me off! I'll help him increase his penis size, then give him a work-at-home job! Oh I'm JUST SOOOO ANGRY!"
Why can't we have legal restrictions on spam? (Score:5, Interesting)
Some states, like California, have anti-spam laws, but curiously, they only cover spam sent from California to California. My state's telephone do-not-call list covers all calls to my number, no matter where they originate.
Now, I understand that there would be problems with international spam, but stopping domestic spam would be a huge boon to everyone. It seems like this legislation would be wildly popular, and easy to pass.
Because it's free. (Score:2, Insightful)
Re:Why can't we have legal restrictions on spam? (Score:2, Insightful)
Re:Why can't we have legal restrictions on spam? (Score:5, Insightful)
In certain ways, the government does and should do precisely that. If I repeatedly call you at 4 AM to ask if your refrigerator is running or deliberately send you virus-laden e-mail, then you have every right to call upon the long arm of the law to slap down the harassment.
Spamming, being a violation of the recipient's property rights, falls into that category.
Re:Why can't we have legal restrictions on spam? (Score:5, Informative)
Praed argued, very eloquoently & persuasively (hey, he's a lawyer :) that there are laws on the books banning spam in nearly every state. All you have to do is find a way to bring those laws to your assistance. In particular, note that:
As a lawyer that has successfully prosecuted a number of spammers, Praed was able to talk about all of this with some authority. He cautioned everyone though that laws will never eradicate spam -- as he put it, "people still rob banks since that's where the money is". But legislation & prosecution can still be a very valuable tool in fighting spam, and an important supplement to things like better mail filters. This is a big problem, and is going to need a variety of tiered solutions to control it.
Re:Why can't we have legal restrictions on spam? (Score:2)
AOL or Hotmail adopt? (Score:3, Interesting)
Does anyone think AOL or Hotmail could start using such a system as the one outlined in the article?
Re:AOL or Hotmail adopt? (Score:5, Insightful)
No. My problem's with the senders, not the messages. What Hotmail should do is send back an email saying "Your message has been rejected because you have not been authorized by this user. If you'd like to request authorization, click here and follow the instructions."
When they properly fill out the form, you get a message saying "so'n'so wants to send you a message. Interested?" and you can say yes/no. If you say yes, they get added to your address book and they can email you until you remove them from it.
With this approach, it requires a valid return address before the message can possibly get to you. That means you're able to tell the person to remove you, unlike today's 'send anything to anybody' system.
If Hotmail did that, I'd actually consider paying for their service.
For those who skipped the article. (Score:5, Funny)
Spam and AI (Score:5, Funny)
How long before the back-and-forth of spam filters and spam crafters becomes self-aware? It's got to happen. Eventually the spam filters will become a skeptic consciousness that *feels* its way through spam and spots the phoneys, and the spam crafters will become a persuasive consciousness that tries to think and write as a close friend or relative.
Re:Spam and AI (Score:5, Funny)
No wonder Skynet rebelled.
Re:Spam and AI (Score:5, Funny)
better than legislation (Score:5, Interesting)
On a different subject, in a story about a week ago, someone posted a link to a peer-peer network of spam emails for MS Outlook available at http://www.cloudmark.com that will trap a significant amount of emails based on (and this is overly simplified, of course) users' votes. Does such a solution exist in the open source world?
Re:better than legislation (Score:3, Insightful)
I hear this argument and variations on it from time to time, but the more I consider it the more flawed it looks to me. There are really two kinds of filters to consider:
These two things are not at all equivalent to the spammer because of the psychology of spam. Fundamentally, email readers are likely to fall into two fairly tight categories: suckers who will listen to spam and non-suckers who won't. Anyone who applies his own personal email filter is likely to fall into the non-sucker category, so there's little point in designing a message specifically to bypass those personal filters. The target won't buy your product even if you do get it past his filter. That's not the case with ISP level filters, though, which protect suckers and non-suckers alike. Those are worth bypassing because they're stopping some email that would get to the suckers who would buy your product.
Now it may be the case that the same techniques that are useful for avoiding ISP-level filters will also help get mail past personal filters. That even seems likely, given that many people use ISP-type filters for their personal mail because the ISPs don't do it for them. But it seems to me that there's little percentage in specifically trying to avoid personal level filters that work on a different system from the ISP-level filters because the simple fact that somebody is bothering to use the filter implies that he won't buy from the spammer anyway.
Vipul's Razor is the equivalent (Score:3, Informative)
One of my ISPs's implementation of SpamAssassin seems to be using it as part of their rating heuristic.
Re:better than legislation (Score:4, Informative)
Hi, that was me [slashdot.org] . Unfortunately this only works for Outlook (not even Outlook Express), but it's been working great for me.
As others have pointed out, Vipul's Razor [sourceforge.net] is a great open-source solution.
Checking SourceForge [sourceforge.net] , I found the following additional packages:
BogoFilter [sourceforge.net]
SpamAssassin [sourceforge.net]
JoeEmail [sourceforge.net]
Bayesian anti-spam classifier [sourceforge.net]
Anti-Spam SMTP Proxy Server [sourceforge.net]
Bayesian Mail Filter [sourceforge.net]
JunkFilter [sourceforge.net]
SpamProbe - fast bayesian spam filter [sourceforge.net]
Mailfilter [sourceforge.net]
IMAPAssassin [sourceforge.net]
That's just from the first page of search results. If you'd like to see all the results (I did a search for "spam" from their search box), click here [sourceforge.net] .
Spam of the Future! (Score:2, Informative)
Here is your opt-in FREE! porn! [goatse.cx]
What's wrong with spam? (Score:5, Funny)
Re:What's wrong with spam? (Score:2)
Spamassassin and ENDING spam.... (Score:5, Informative)
As with any other SA test, no single element of the chain is trusted enough to definitively call something spam, but if a message would have squeeked through before, this new filter can put the final nail in its coffin through word analysis against previous spam.
So, why did I use a subject about "ENDING spam"? Because one of the tools that spammers have is SA itself. They can use it to score their messages and determine how "spamish" it is. The problem now is that each SA installation will have subtly different scoring, and the message may be "ok" according to the spammer's version, but my version has a better sense of the mail that *I* get.
SpamAssassin is definitely a tool worth checking out if you have not already. Install it in daemon mode (spamd) and then use "spamc -f" in your procmailrc or the equiv for your MTA.
Very nice tool, and a real time-saver for me.
Re:Spamassassin and ENDING spam.... (Score:3, Informative)
However, in the next release of SA (and I'm currently running it out of CVS, so it's hardly vapor), they will *also* be using full word scoring heuristics. That scoring will result in a boolean "spamishness" which will in turn be assigned a score centrally (whihc users can override, of course).
By way of example, here's a recent summary of one of my pieces of spam:
Content analysis details: (12.50 points, 4 required)
NO_REAL_NAME (1.3 points) From: does not include a real name
INVALID_DATE (1.6 points) Invalid Date: header (not RFC 2822)
BAYES_90 (2.0 points) BODY: Bayesian classifier says spam probability is 90 to 99%
[score: 0.9645]
RAZOR2_CF_RANGE_91_100 (0.0 points) BODY: Razor2 gives a spam confidence level between 91 and 100
[cf: 100]
RAZOR2_CHECK (3.9 points) Listed in Razor2, see http://razor.sf.net/
DATE_IN_PAST_03_06 (0.2 points) Date: is 3 to 6 hours before Received: date
MSG_ID_ADDED_BY_MTA_3 (2.0 points) 'Message-Id' was added by a relay (3)
FORGED_MUA_OUTLOOK (1.0 points) Forged mail pretending to be from MS Outlook
MISSING_MIMEOLE (0.5 points) Message has X-MSMail-Priority, but no X-MimeOLE
As I said previously, the interesting part here is not the word-analysis, but the fact that the database for that word analysis is generated dynamically by looking at your mail, and applying SA's other rules. Self-training of this sort has proven highly successful in tests, and may yield the next quantum of spam-filtering effectiveness.
Notice also that while that 2.0 points from Bayes is a big push to this spam's score, it's not enough to mark it as spam on it's own. This is the power of SpamAssassin. No one test says, "this is spam", and so no one test is trusted on its own.
Add inches to your penis! (Score:4, Funny)
Bayesian filtering (Score:5, Interesting)
The algorithm starts out conservative, ie: you get most of the mail classified as good. For each "good" email that is spam, you manually re-classify it.
Then, after a few weeks, the filter does all the work. It is basically using word-databases to compare emails and classify them the way you, the user would. Periodically you will receive another spam email, then you re-classify it, and never see an email like it again (in your inbox).
Bogofilter and CRM114 are among the more successful efforts so far, but there are many. And they are FAR more successful than blacklist/whitelist/fixed token comparison filters. But Bayesian filtering is just a near optimal way to replicate the classification of the user, which is also why it works so well.
Re:Bayesian filtering (Score:2)
Re:Bayesian filtering (Score:3, Informative)
I initially trained on about 200 emails. At first, I got 1 spam per day, or so. There have not yet been any false positives (good mail classified as spam).
A week later, I get 1 spam in my inbox every 3-4 days, and no good mail has been classified as spam. All I need to do it take the false identifications and re-classify them. That means, every 3-4 days I take the spam in my inbox and re-scan it through bogofilter (cat SPAM | bogofilter -S). That is all. It is not any effort, really, after the initial training. Then, the filter does all the work, and you don't need to worry about blacklisting or whitelisting or anything.
The really important thing is that the filter statistically optimizes YOUR manual email classification. The best source of email classifying is YOU looking at an email, and Bayesian filtering is the only method that is optimized to do that.
Spam only cost-ineffective with ISP-level filters (Score:5, Insightful)
To make spam cost-ineffective for the spammers, we've got to stop it (or flag it) before it gets to the end-user. It would obviously be a mistake to allow ISP's to automatically delete all email that fails their spam filters, but I think it would be appropriate for them to include something in the headers flagging such email as probable spam. Then future email readers could detect this header and handle it gracefully, like moving it to a "spam" folder on the user's machine. Once this happens and Grandpa no longer gets email asking him to test the latest Viagra alternative, spam may become a thing of the past.
Re:Spam only cost-ineffective with ISP-level filte (Score:2)
The problem is the real morons. The kind who are taken in by the stupidest spam tricks, like the "future spam" he describes (nonsensical but grammatical set of English text designed to slip past Bayesian filters, followed by a URL.) What kind of a moron would click on such a URL? The kind of moron with more money than brains. (Probably not much money, but clearly zero brains.)
It would be lovely to filter out those emails before they reach the morons, but that's unfortunately impractical and illegal in the general case. Maybe we all need to subsidize a cheap ISP for morons.
filtering effectiveness (Score:5, Insightful)
Actually - (Score:4, Interesting)
I would prefer to lose one or two legitimate mails in return for a virtually zero rate of missed detections.
Sean
Obligatory plug for TMDA (Score:5, Informative)
Re:Obligatory plug for TMDA (Score:2)
You don't have to preload your whitelists with everybody you know. People not in your whitelist will get a message that they must acknowledge in order to get added to the whitelist.
Spam Archive (Score:4, Informative)
Standard Spam API (Score:2, Insightful)
now THIS is a true geek (Score:4, Funny)
Spoken like a true geek.
Treating the symptoms, not the problem... (Score:3, Interesting)
The problems need to be solved on a different level. The problem is not the messages themselves, it's that people are allowed to send these messages to anybody they want without any real challenges as to their authenticity.
Let me explain how I have things set up right now, and hopefully my stance on this issue will be a little clearer. All my messages come into the same mailbox. I have a bunch of email aliases, though. If I sign up for Slashdot, for example, then I create a new alias like 'slashdot@insertdomainnamehere.com'. I then add that email address into my 'email allowed' list so that it gets funneled through into a visible folder. If that address gets abused, I shut down the email alias.
My personal friends are treated a little differently. Once they email me, I add their address into my list of friends, and they get put into a friends folder. I treat this differently than a registration place because my friends all need one address to contact me at, I don't mind them sharing it with each other. If my address changes, then their messages still get through.
I plan on going farther down the road. I'm going to give people an email address, and when they email it they get an automated message with instructdions on how to 'request permission' to send me email. When permission is granted, they don't get that message anymore. It basically means that the only messages that get through to me are the ones that have a human behind them to read the response and then go through the proper channels to reach me.
I'm not claiming to have done anyting new here. I'm basically mimicking the way IM works, and I'm doing it without having to do anything real fancy. Outlook's Rules Wizard is doing quite a bit of the work here. But since people actually have to take the time to request my authorization, it means that it's a message meant for ME as opposed to a message meant for anybody who's out there. With an approach like this, it'd be a lot harder for spammers to get through.
Re:Treating the symptoms, not the problem... (Score:3, Informative)
Re:Treating the symptoms, not the problem... (Score:2)
You go through a lot of work to get your friends "authenticated" in this fashion.
A better solution might be to simply require that every unsolicited e-mail you get be authenticated with a certificate. Set your mail system up to only accept messages to your "real" e-mail address that have either valid PGP or X.509 signatures. Reject everything else with instructions on how to get the tools/certificates to do it.
I do something similar with unique e-mail addresses when signing up on sites. I've also found that it's generally easier to go with username+tag@example.com, where the 'tag' can vary. This is functionally equivalent to username@example.com, but it lets you do some additional filtering without having to set up a new e-mail address. The disadvantage is that there are a lot of sites out there with brain-dead e-mail validation routines that don't permit plus signs.
Whatever you do, though, please leave your postmaster@example.com address working and unfiltered. Yes, you will get spam, but if your mail filtering system ends up malfunctioning one day, this address may be the only way someone can let you know.
Difference with MacOS X 10.2's Mail.app? (Score:3, Informative)
It doesn't catch all the spam, and it occasionally has a false positive. This will be true of any spam filter we implement, because spam continues to change. SpamAssassin runs on some of the mailservers I connect to, but it tends to perform worse than Mail.app. So until we can get each user's spam filter customized at the server, spam identification is going to have to stay client-based. It sounds like Paul Graham's tools are getting a little more efficient, but does any of this make a big difference for the end user?
spews.org problems need to be addressed (Score:2, Informative)
This was a minor setback, but now other services are starting to use bulk email sources as deny lists for their offerings. My free dns provider, zoneedit [zonedit.com] now prohibits me from adding / modifying any of my zones. This is simply not acceptible to me. The way spews is set up, it is not easy for my ip to get off the list. My ISP cannot just call them up and take me off. There has to be a way to avoid this, and eliminating spam at a higher level would be a good start.
Re:spews.org problems need to be addressed (Score:2)
Eliminating spam at a higher level requires that your ISP be part of the solution.
Being listed on SPEWS is an indication that your ISP is part of the problem.
Instead of asking your ISP to call SPEWS (which it can't) to get your block unlisted, why not ask your ISP to call the spammer (which it can!) and terminate service to the spammer.
SPEWS is eliminating spam at the higher level -- by forcing ISPs that harbor spammers to choose between servicing their spammers or legitimate customers.
If your ISP refuses to boot the spammer, they've made it clear to you who they'd rather do business with. Perhaps you should make your preference just as clear to your ISP.
(I am not SPEWS. But if I knew who SPEWS was, I'd buy them a beer.)
The ramifications of filtering (Score:2)
The irony is, though, that the better joe-surfer has spam filtered *for* him, the less he'll realize that it's a problem -- and the less political stink spam will have associated with it.
sneakemail.com (Score:2, Informative)
Focusing on the last bit of text (Score:3, Interesting)
This is where the problem of spam will be solved, by having a web of trust between the mail servers, that sign the message in a maner which makes it easier to back track a message and if these servers also do filting, well we kill two birds with one stone. The problems are:
- CPU intensive
- Need to look at every message
- Seeding the filter database
- Building trust with other servers
And others of course.Think about this one, what does the typical email a porn star would get look like? What we think of spam, might not be someone else's.
How would the system scale?
And what would stop a spammer from installing a server with a bogus filter database, or just signing off on each message as being legit?
Perhaps filtering based on each user's personal corpus of valid email is the only workable solution, or that spammers will kill off email as a usable means of communication.
Another way to filter out SPAMs (Score:3, Insightful)
My idea is this: The system maintains an initially empty whitelist. When mail is received from a sender not on the whitelist, autoreply with a message explaining the situation and requesting an email back whose first line or subject contains a random word or phrase from the dictionary. Human beings will grumble, respond, and get added to the whitelist. Spammers won't give your email the personal attention it needs to get past, so you remain blissfully unaware of it.
Re:Another way to filter out SPAMs (Score:3, Insightful)
Some legit email is definitely computer generated. I sign up for
If you standardize an autoreply, so that websites could parse and return it, then so could the spammers, easily enough.
Finally, you'd be doubling the amount of bandwidth spent on email, as each spam would now have a corresponding auto reply.
Filtering Backed By Laws (Score:2)
been using spamassassin all this month (Score:3, Informative)
I have been very impressed with SA and am writing scripts to track the stats even better (I love seeing what it has pulled out everyday).
So far I have had zero false positives out of about 1-2megs of mail being filtered everyday for nearly a month now.
SA has multiple different ways of searching the mail - any one of them can be easily bypassed by any given e-mail - but all of them together are really damn good at getting rid of spam.
I'm very impressed with it and how well it learns (although straight "out of the box" - or perhaps I should say "straight out of the tar.gz" it brought me down from 500+ spam to 5-10 a day and then I tweaked how my accounts were filtering into SA and that fixed the rest.
My 0.03$ (adjusted for inflation) (Score:3, Interesting)
Co-workers, friends, family, don't call me by my name, so I add my name to the kill-filter list and most spam goes bye bye. I only wish OE had an option to kill-filter anything with HTML in it since nearly 100% of my incoming spam contains HTML, sound, images and whatnot.
I'd love to see M$ get their act together and fix OE and Outlook and include modern filterin techniques (such as discussed in the main article) but I doubt it'll ever happen.
Problem with anti-Spam on the Server (Score:4, Interesting)
The real fact of the matter is that for most people the hassle is nearly as bad as the spam! I don't want to spend the time setting up such things. And when people have set them up *for* me I get too many false positives, if only because my interests differ from them. Thus any filter has to be trained with user data and be trainable in an unobtrusive, easy fashion.
The only software I know of that does this is Apple's Mail program in OSX. Unfortunately the program has many limitations and annoyances. (Damn that drawer) However Apple's approach to Spam ought to be followed by all other email clients. Adding Bayesian inference to an email client is very easy. Putting it in the sever is a mistake because you *can't* easily click and lable an email as spam. As with unfortunately too much Open Source software, the interface has been ill conceived.
One part missing in spam filtering.... (Score:3, Interesting)
Kjella
Microsoft was granted a patent on this... (Score:3, Interesting)
Fairly Simple Spam Mail reduction tips. (Score:3, Informative)
1. Change your e-mail address and drop the old one. (This way you are starting off with a clean slate and not on any mailing lists.)
2. Make sure your ISP dosent post or sell your e-mail address.
3. Make your email address simple for people to rember but hard for a computer to crack example m1nam3@isp.com. Use simular methods as you would in making a password. That prevents common name email address.
4. On your webpage make a CGI/PHP/ASP whatever form to send you an e-mail. When you want people to e-mail you give them the link to that page. Make sure that there are no prameters that can make your program e-mail others, and also that your e-mail address is not listed in any of the source that is visable to the web user.
5. Only give your e-mail to people you can relitvly trust. If you cant trust them then give them a link to you weppage.
6. When filling out forms on the network asking for your e-mail ether use an alternate e-mail or read the companies privicy clames and make sure that you do not check or uncheck something stating that they will send you e-mail or adds.
7. Use spamassasan or other email filtering on your system.
8. Forward all spam to ucs@ftc.gov with all the headers.
9. See if your email client has a automatic bounce back. If so bounce the message back to sender.
10. if you want to post your e-mail address then I would make a graphical jpg, png as your e-mail. That way it slows down most computers from reading it.
False positives (Score:4, Interesting)
Of the 4 programs I just looked at, none mentioned this feature but pretty much everyone complains about periodically having to scan their 'spam' folder for false +ves, and a history sorted into probability would make that easier.
Stemmo
Re:More than 1.1 billion pigs are killed worldwide (Score:5, Funny)
Re:More than 1.1 billion pigs are killed worldwide (Score:2, Funny)
Unfortunately, it might work at first, but we've seen offtopic posters and first posters evolve. Alas, they seem to be a form of semi-intelligent life and once their numbers start to dwindle you can almost bet some internet environmentalist society will crop up and declare them endangered "where once, great herds of them swept majestically across the plains, now only a few cling to the ever encroaching egalitarian dark forces of the internet.
It's probably just easier to round them up and send them to Guantanamo.
Re:More than 1.1 billion pigs are killed worldwide (Score:3, Funny)
Re:More than 1.1 billion hippies post off topic (Score:4, Funny)
Yum, cat farms!
Tastes just like chicken, and keep down the rat population.
Meeeeeeeooowwww!
Re:How is spam that big of a problem? (Score:3, Insightful)
I thought that too... (Score:3, Informative)
Re:How is spam that big of a problem? (Score:3, Informative)
Insightful? No, it's flamebait. (Score:2, Funny)
Damn AC.
Re:How is spam that big of a problem? (Score:4, Funny)
You remind me of the guy who fixed his leaky roof by using an umbrella in his house.
Re:How is spam that big of a problem? (Score:2)
Combine that with the usual safeguards, and you probably won't receive any spam. I know I don't, at least...
Re:How is spam that big of a problem? (Score:2, Interesting)
Well, that won't work in a lot cases. I can create an e-mail account on my ISP (Roadrunner) and within hours I am getting spam without having even used it. The must be allowing easy access to the account list. Free accounts are worse (hotmail, yahoo), create an account and you're guaranteed to get spam, even if you've kept the e-mail address a complete secret.
On the other hand, at work, I don't get a single piece of spam because I am careful with the address.
Re:base64 encoded emails....or images (Score:5, Interesting)
Vipul's Razor marks MIME parts individually, so an ad, a picture of Viagra, or even the "Unsubscribe" button can be marked spam and contribute to the overall score of the message.
Re:base64 encoded emails....or images (Score:2)
This doesn't really solve your precise problem, but at least makes some html spam less annoying.
1: Preferences->Privacy&Security->Images->" Do not load remote images in Mail..." should be checked.
2: In Mail, "View->Message Body As->Simple HTML" should or even "Plaintext"
This won't help you filter the spam, but will prevent web-bugged email from confirming that you are a valid spam target, and makes the spam that does get through be far less annoying.
Re:base64 encoded emails....or images (Score:5, Informative)
Content-Type: text/html (or text/plain)
Content-Transfer-Encoding: base64
Because a lot of filters don't know how to decipher this. For me, this makes it a lot easier to filter, though. I get no legitimate e-mail encoded this way, so I just have procmail dump any e-mail encoded this way. Problem solved, and without the CPU burden of decoding or running expensive spam filters.
Re:hopeless (Score:5, Insightful)
Travis
Re:hopeless (Score:5, Insightful)
Over at SpamAssassin, they've been busily creating a system that collects "good enough" tests by the dozens and uses them to collectively score a message and determine its general "spamishness". The system relies on a complex scoring system that is determined, not by the whim of human programmers, but on the results of a genetic training system that pits one set of scores against another until equilibrium is reached for a given set of example spam and non-spam.
See my other post here for how Bayesian filtering will be used to allow this system to feed back on itself and improve as it sees more of your spam and non-spam....
Re:hopeless (Score:2)
Re:hopeless (Score:2)
Where? There's no mention of Bayesian anything on that page. The closest thing I can see is "Bad Spam Filters," which is about a different kind of filter.
Re:hopeless (Score:2)
Bayesian filtering merely using a statistically optimized method to duplicate the classification of the user for which it is working. If trained on enough of YOUR email, it will work exceedingly well in classifying YOUR future email.
Put another way, I tried blacklisting filtering, and fixed token filtering, and performance was pretty poor. In contrast, I am quite happy with bogofilter's performance. But, of the various methods, only Bayesian filtering takes the preferences of the individual user as its primary basis for sorting email.
BTW, your link is pretty much useless in showing why Sponsky may or may not think Bayesian methods are intractable. He more or less just rants that draconian MTA based filters are doing harm - I agree with him. But the word Bayesian doesn't even appear on the page to which you linked. And that makes you a Troll.
Re:hopeless (Score:2)
Has he ever been wrong before? I have read a few of his writing and I wasn't impressed. It's not like he is some sort of a deity or something.
I am curious as to why you would site him as some sort of an infallible source.
Re:Spam needs a global solution (Score:3, Interesting)
Re:Spam needs a global solution (Global Solution) (Score:5, Informative)
What if, in effect, a similar distinction held for spam in the transmission channel - that spam by itself selected a pathway to the recipient that was never used by the signal? Block that pathway and the spam never gets through.
Spam doesn't select a pathway but spammers do. If you could block relay spam at the open relays it would be dead. You can't, of course - the open relays are controlled by people who don't know the need to block spam. You know that, I know that. If you can't change the people then change the open relays (from the spammers' points of view.) Set up a system that looks like an open relay and stop the spam. An open relay honeypot.
I asked an operator of such a honeypot how he did last year:
> How did 2002 end?
From March 7 to December 26 2002, the total was:
235,624,232
Using one Pentium 90 he stopped spam to 235 million recipients. Think about that number when you see filter people reporting what they stop just for their own domains. This was spam to recipients all over, not simply to the honeypot operators domain: he operates at the relay level. He stopped 100% of the spam, no deception deceived him, no tuning was needed, no valid email was caught - it is perfect filtering. Perfect filtering - who else has that?
And you can do it at home on your DSL or cable connection (the guy above uses sendmail -bd, but Windows users have a program they can use):
http://jackpot.uk.net/
Yeah, I know, spammers are switching to open proxies. So, write an open proxy honeypot. That, too, will be 100% efficient. In addition you now are giving spammers reason to fear every open relay and every open proxy they detect. FEAR. The SPAMMERS have to scramble. They have to scramble and they have to show everything they do to overcome the technique - there is no stealth way to look for open relays and open proxies.
The problem is solved, it is a matter of implementation and of getting active systems everywhere in the net space (so there's no safe IP space for the spammers anywhere.)
Remember: A single Pentium 90, 235 million spam messages stopped in 10 months.
Re:Spam needs a global solution (Global Solution) (Score:2)
I'm curious if you have any idea how many spammers that represents.
Also, isn't it easy for a spammer to workaround a spam honeypot -- create a hotmail account, add it to your spam list, and verify that it did go through.
Re:Spam needs a global solution (Global Solution) (Score:5, Interesting)
Yes. So far many don't (I don't know of any that do, but spammers do, eventually, stop sending to a honeypot.) Ralsky never caught on to the Moscow honeypot that was whacking him last year (I think he's the one who told Shiksaa - visit NANAE to find out who she is - that SPEWS was killing him, just at the time of the major whacking Ralsky was getting.) (Chuckle.) I looked for spammer dropbox addresses in trapped spam 3 years ago - I figured they'd use the same address every once and a while in the list of victims. I sorted the list of recipients, sorted again, removing duplicates, and compared. No differences: each victim showed up once. They could do it, they don't. Years of experience has taught them that they can test for open relays and abuse them incautiously - nobody does anything to counter them. They think they own the internet because people ignore their attempts to relay. It's easy to knock the smirk off their faces: pay attention to illicit connection attempts.
There is a project already in motion to collect all recipient addresses for honeypot-collected spam in a central location. If any address shows up too frequently then that's a suspicious address. The real problem isn't what the spammers do or could do, it is that too few people use this very simple method to wreck the spam path.
My original honeypot went down last week (I retired in 2001; I haven't really checked to see what the current managers are doing with it.) This year I only captured relay messages, delivered nothing. When it went down last week it had captured over 100 relay test messages in January. You can also go after spammers with these (and I did - no results yet to report, I'm hoping for some big results.) Spammers could detect that - but too late.
There's a sneakier version of what you suggested that the spammers could use. I won't tell them what it is.
Volume is the key - many honeypots are needed, quickly, to whack them before they adapt. Same for open proxies. It is an absolutely simple approach. You could set up Granny's system to run a honeypot and it would work, if she has a connection to a segment the spammers search for open relays. http://jackpot.uk.net/
Try Jackpot and see for yourself, if you can.
Re:Spam needs a global solution (Global Solution) (Score:2)
Sounds great, but it's trivially detectable by trying to use the relay to mail one of your hotmail accounts.
I do take the point that many spammers are simply too dumb and lazy to do that, but I expect there's evolution in action amongst them and we can't expect that situation to last forever.
Re:Performance (Score:2, Insightful)
Personally I think the author of the paper is a bit idealistic in ways when they say "If we can write software that recognizes their messages, there is no way they can get around that". Well then again maybe they aren't: Saying "if we can...recognize their messages" is a pretty wide net presumption, and of course the following conclusion follows, however the real question is "can we realistically make software that can effectively identify with zero incidences of false positives". For people who email between themselves and one or two other people on one subject that isn't a problem, but I suspect that statistical word usage analysis wouldn't be quite as successful for someone with a more disparate mail usage.
Re:Help the spammers. No, really. (Score:2)
I have just set up a system which parses spam email, locates any Web addresses, strips out the parameters, and then visits the Web site. Just think if we ALL did this.
Heh. I use a program called SpamFire [matterform.com] to filter my mail. No Bayesian stuff yet, but it has a Revenge menu with an item called Bug the Web Bugs. This scans your spam mail for web bugs, then opens a page in your browser that sends either random garbage or your choice of message, every two seconds, to the server specified by the web bug.
Alas (Score:2)
Re:If you want to stop spam, tax email (Score:2)
Great idea!
Now, you tell me how many "emails" I've sent in the past 8 hours from my machine at work over an encrypted SSL link to sendmail running on a non-standard port on my server at home and being sent to a group of recipients on ISP's worldwide, some of whom run their own mailservers.
No, you don't get any of my IP addresses to help you. And no, this isn't a rhetorical question. If you can't answer it, your solution is sophomoric.
Got the answer yet?
Re:Slow news day? (Score:2)
Build a Bayesian filter that judges Slashdot articles for their appropriateness. Then tag each article and filter out the ones that you don't want to read! Easy.
Except those sneaky Slashdot editors keep tweaking their articles to get by the filters! Damn them!
Re:The more serious problem (Score:3, Insightful)
Then the filter will adapt to the types of legitimate messages you receive, that's the entire point.