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Two Spam Filters 10 Times As Accurate As Humans
Nuclear Elephant writes "The authors of two spam filters, CRM114 and DSPAM, announced recently
that their filters have achieved accuracy rates ten times better than a human is capable of. Based on a study by Bill Yerazunis of CRM114, the average human is only 99.84% accurate. Both filters are reporting to have reached accuracy levels between 99.983% and 99.984% (1 misclassification in 6250 messages) using completely different approaches (CRM114 touts Markovan, while DSPAM implements a Dolby-type noise reduction algorithm called Dobly). If you're looking for a way to rid spam from your inbox, roll on over to one of these authors' websites."
Comment removed based on user account deletion
I presume they mean more accurate than a human that was only looking at the subject line? I fail to see how someone could misclassify an email after they'd already opened it unless it was some kind of marathon testing, which would be totally unrepresentative of any real life situation. Once you're getting 6,000 messages, it's time to reach for "Delete All" and change your address, methinks
I suppose it depends how you're defining spam. Perhaps the ultimate spam messages that don't get past them are capable of passing a turing test... hence fooling those gullible human recipients into thinking that it isn't even spam!
Fortunately, soon we will all be able to use the superhuman spam-detection capabilities of these filters to save us from ourselves. Imagine all of those pesky e-mails from your 'friends' getting caught by your spam filter before they even impinge upon your consciousness.
It'd be a wonderful world.
"We have to go forth and crush every world view that doesn't believe in tolerance and free speech." - David Brin
If you read the post, it quotes a study and says humans are only accurate 99.84% of the time.
:)
Kinda makes you wonder how they can know the filters are right though.
(please don't reply telling me how)
Obviously you've never seen someone new to the internet sit in front of their computer. Lots of people don't know what popups are. Lots of people read some spam not knowing what it is. To these people, a computer is merely an interesting string of sensations.
Know what I like about atheists? I've yet to meet one that believes God is on their side.
I'm also sure that Yahoo's "SpamGuard" was great when they first introduced it. Now, It catches roughly half of all the spam I get. Why? Because people have figured out how it works and taken advantage of it. The same will happen with any content-recognition-based spam software. In the extreme case, even if a piece of software were 100% accurate at saying "This piece of e-mail looks like spam," then spammers would just make their e-mails look exactly like e-mail from one of your buddies. How could software ever tell the difference between:
Hey, dude, check out this website I found. There are some hot naked chicks and stuff. Sweet.
Signed,
Your Buddy
and
Hey, dude, check out this website I found. There are some hot naked chicks and stuff. Sweet.
Signed,
SpamKiddy
Even a human can't tell the difference. The only real difference is who they're from.
Perhaps they mean that Human A is reading email intended for Human B and attempting to classify the email as spam or not spam. I wouldnt be surprised if a computer could do a better job at that sort of task. Besides Im sure Human B wouldnt want Human A reading that cyber sex chat log.
It's really easy to design an effective solution when the problem is purely mechanical or natural. As long as you're working with spammers who don't adapt, you can slice through their shitstorms very effectively.
But when a single solution becomes mainstream, spammers will adapt to it. Bayesian filters tend to work very well, but now spammers are adding sprawls of randomly generated green-light text to offset the filter's score.
Google found an excellent way to rank websites, but then it became widespread enough that webmasters began to game the system it had created. It's been playing catch-up ever since.
Once the adversary begins to adapt, we lapse into the same cat-and-mouse game of technological barriers and counter-barriers that we've seen so many times before.
Quite simple:
With 10 messages (after automatic spam detection) humans are 100% accurate.
With 1,000 messages, (before automatic spam detection)
humans are less than 100% accurate.
The experiment was done on 5849 messages.
Remember; one thing computers are good at is doing boring things repeatedly.
Results of new spam filters cannot help but to be bogus... The true test of a filter is how well it works *after* all the spammers know how it works and try to circumvent it.
Enjoy science fiction? "Turing Evolved" - AI, Mecha, Androids and rail-gun battles. What more could you want?
client/server-side filtering does NOT solve the problem!
The biggest problem with spam is the invasion of third party computers on the Internet. The ILLEGAL activity spammers perpetrate by breaking into machines, forging headers and hijacking servers.
Any filtering method does not address this most serious problem, and even if you do not see any spam in your inbox, you're still paying for the bandwidth and system resources these spammers steal.
Stop with the filtering algorhythms and take some of that energy and contact your local Attorney General, DA and FBI and demand that they prosecute these people who are BREAKING THE LAW.
They are learning algorithms. For measuring their accuracy you have to assume that the data is correctly classified so you can see how they do.
The point is that humans also aren't perfect. Have a person classify 10000 emails and they will make a few mistakes. Point out those mistakes, and they will say "yes, I got that wrong it is an email from my wife reminding me to pick up milk and not a spam trying to sell me printer ink, I must have been day dreaming."
Just like if you give a person a document and say "find all the spelling errors" they will probably miss some. This is not because they have a different definition of how those words are spelt, it is because they made some mistakes.
For the training/testing data, some double checking needs to be done to find the mistakes the human classifying it almost certainly made.
It's a pretty normal situation in any machine learning application, you don't have to be perfect to be as good as a human - after all humans are only human.
Saying an algorithm is x% accurate is not sufficient, because there are two kinds of errors: false acceptance of spam, and false rejection of non-spam. Personally, I'd settle for 90% false acceptance if I knew the false reject rate was 100% rather than have a program that was 99% at both.
Also, I wonder how many people have actually looked at CRM114 and tried to use it.
The really interesting thing about CRM114 is the windowed polynomial hashing technique used although there's some evidence that it can work just as well (if not better) on a much smaller window of only two tokens. I'm hoping someone will do a full exploration of the idea for SpamAssassin's Bayes module.
No, humans aren't 100% and yes, you can test for that. Try a thought experiment: fill a bin with 50,000 red balls and 50,000 blue balls. Ask a human to sort them all. The result probably won't be 100%, but you can still check the result and figure out how accurate the human is without relying on a superhuman ability to tell the balls apart. Same thing for spam: if you start with a known training set, you can test humans to see how well the spam is identified by manual sorting.
I'm not surprised a filter beat the human, considering the study used a sample of 5849 messages. As the sample size increases, the filter's accuray will increase, and the human's will decrease. Furthermore the higher the spam/real ration, the better the filter will do in comparison to a human trying to sort at a reasonable speed. The reason being humans tend to skim, and rairly actually read entire subjects, much less messages. Give a human 5000 messages and an hour and he will probably make some mistakes. On the other hand, in 10 messages, the human will probably be 100% correct. Most email filters rely on this already, as they tend to err on the side of caution. With the bulk of the spam taken out, it is not a burden to have the human check the iffy bits. Furthermore the type of email can mislead humans. A business-type email sent to someone's personal email is much more likely to be mistaken as spam, and vice versa. The main disadvantage of automated filtering is people generally have an idea of when a really important e-mail is going to come (the type that false positives are completely unacceptable) and who it will be from.
Congratulations, Mon Ami.
You have just unlocked the secret of virtually every news report that says "ten times more likely."
To get cancer. To have a heart attack. To suffer from the heartbreak of psoriasis. Whatever.
Yes, these numbers indicate "10 times better," and if you were to ask the reporter how likely am I to avoid cancer in both situations, these are the sorts of numbers he would show you.
Eat health food and your chance of having a heart attack is 99.984%. Eat too many donuts and your chance of having a heart attack is 99.983%, 10 times worse!
Always, always, always ask to see the raw numbers so that you know what "10 times worse" means.
Then ask if the numbers were collected by phone survey. If they were, throw them all away and have donut and a cup of coffee.
KFG
The post quotes "a study" which gives the 99.84% figure. In fact, the 99.84% figure is mentioned in the one paper as "the human author's measured accuracy as an antispam filter...on the first pass". This is what we who understand statistics call "nonsense". An individual human had an estimated accuracy of 99.84% when looking at one particular sample set of data, once. This is not a meaningful number, and it sure as heck ain't "a study".
But the computer reads the entire message, so it's not really a fair comparison, is it? How many more lines of information was the computer allowed to look at to create its superior result?
Karma: It's all a bunch of tree-huggin' hippy crap!
What you're planning has already been done, it's called TMDA, and it's not such a good idea. You're going to send out 800 "challenge" emails per day - have you given any thought to how many of those will be genuine addresses, but have nothing to do with the spam you receive because they just happen to be the joe-job victim? These kind of challenge/response systems may slighlty alleviate your own suffering through spam, but at a cost to all those unfortunate enough to have had their email addresses faked. And if the sheer impoliteness of such net behaviour doesn't put you off, note that you're using up more of your own bandwidth to send out such challenges
If any of the smtp exchange or address lookup fails, just forget it, they're probably not real anyway
It would make a lot more sense to make these kind of checks when you're receiving the email in the first place. Reject at the SMTP level - you never accept and process the spam in the first place
My next sig will be ready soon, but subscribers can beat the rush
$5 / month hosted VPS on linux = awesome!
Not comparable. The job of a junk mail filter is to drop things I don't want to read. It is trying ot match my evaluation, not to match a semi-objective criterion like red or blue.
If I read 1000 messages and say which I wish I hadn't read, then I am 100% accurate by definition.
Of course, if they are really talking about a pure spam filter -- ie one which identifies unsolicited commercial email -- then they can be more accurate than me, but at an uninteresting, perhaps even counter-productive, task:
I may get unsilicited commercial email I do want to read one day. Almost happened once (I had inadvertantly signed up for it, so it was not really unsolicited, and I didn't actually buy the piece of kit they had on special offer that week, but was tempted). I also get stuff I don't want which isn't spam (notably email from virus infected machines).
The referenced study seems to be a very sloppy job from this POV. They don't define what their criterion of sucess is, and to the extent they put in a hand waving attempt it is clearly nonsense:
`Unsolicited' does not imply `not desired'. If they don't tease those two apart, they can't get interesting results for real world applications. Eg, someone mailing my work address with a commercial proposition may well be a very welcome unsolicited commercial email._O_
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