Using AI for Spam Filtering (w/ Source Code)

jarhead4067 writes "Article snippet: "Up until recently, most researchers in the fight against spam have failed to classify it as an artificial living organism, hindering the development of effective tools and techniques to kill it. While this classification may sound strange, consider the following..." A novel approach to filtering spam, and hey, there's free source included."

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[cs02rm0]

Google cache

Animal Rights Activists

[toetagger1]

"living organism ... and techniques to kill it"

Next thing we know, we will have Animal Rights Activists in Washington, D.C. protesting our "spam traps"

Is it any wonder it mimics humans???

[Shoeler]

I mean - hello, humans create it.

We're not up against a new being - it's the same type of beings that create scripts for the hell of it that wreak havoc on computer networks because 1) "We can" or 2) "To show them their weaknesses".

It was a very interesting read for sure - the genetic marker bit was quite interesting. Admittedly though I got about 2/3rds the way through it and lost interest.

Blame the spammers I say. ^_^

How is this news ?

[janoc]

How exactly is this news ? It seems that the author of the neural network idea didn't do his homework - e.g. DSPAM includes neural network as an experimental classifier already. And compared to the proposed C# solution, DSPAM is a widely used and mature product already.

Regards, Jan

Not new, not genetic, not A.I. -- it's Bayesian

[orthogonal]

Is Slashdot trying to jump the shark?

We already saw a plagiarized article green-lighted, and now this? Cmdr Taco, Slashdot was a brilliant idea of yours, and I love your site -- but that's because I have reasonably high expectations for it.

First, the submitter of this article has he email address jarhead4067@hotmail.com -- and so does the article's author.

Second, what is presented is not a genetic algorithm. The characteristics of the email to be considered to discover if the email is spam are finite and hard-core -- and even the threshold some characteristics must reach to qualify as spam are hard-core:

// This can be adjusted... Calculating the misspelled word ratio and
// any Bayesian probability is time consuming
if (stats.SpamProbability < .66)

A genetic algorithm is one in which the goal is hard-core, different means of reaching that goal are generated, and the characteristics of the most successful are used to generate the next "generation"; this is repeated until the goal is reached.

But in this model, each "chromosome" contains statistics about one email. The heart of this model is to train a neural network with known emails ("chromosomes") and then tests unknown emails ("chromosomes") against the network.

Neural networks have a checkered history in Artificial Intelligence research. A (very much simplified) model of biologic neurons, neural networks were for a time seen as a great hope for Artificial Intelligence. A neural network basically starts out with an array of input nodes and an array of output nodes, with each input node connected to each output. Each input corresponds to some characteristic of the items the network is trained with: for classifying animals, the inputs would be characteristic of animals, e.g., "furry", "bipedal", "feathered"; each output a classification, e.g., "mammal", "bird", "human".

To train the network, the input nodes are set to the characteristics of an item, and then the strength of the connection of those inputs to the correct outputs is increased (or that of other connections is decreased -- it's the same thing). With enough training, it's possible to isolate the salient characteristics from the ambiguous one sin a mechanistic way.

This is useful, but it was soon discovered that these simple neural networks, for certain sets of inputs, failed, because of overlapping categories: both birds and humans are bipedal, but only humans are also mammals. In a single layer neural network, the connection strength between input "bipedal" and output "mammal" would fluctuate, unable to describe humans or birds well. These problems can be alleviated by adding additional "hidden" layers of nodes between input and outputs, and by allowing "back-propagation" from output or hidden nodes to layers "previous" to them.

But even with these enhancements, it's been conclusively shown that some problems are intractable for neural networks. In any case, neural networks are no new thing.

Of course I have no idea if classifying spam is intractable or not, but I have to question whether using a neural network reliably can outperform Bayesian (or quasi-Bayesian) filtering. My guess is that since Bayesian filtering can judge email by the occurrence of single tokens ("words"), and not just "chromosome" statistics, and given that this "new" method also uses Bayesian filtering to generate one of those "chromosome" statistics anyway (and for only the most difficult to characterize emails to boot), this method itself probably mostly relies on its Bayesian sub-component.

So I'm a bit at a loss to see why this method is in any way revolutionary or even particularly interesting, or why it was green-lighted for Slashdot. Of course, I only gave the linke

How is this any different...

[Fooby]

from SpamAssassin? It takes a bunch of rules, applies them, and uses a neural net to classify the message. Seems to me SpamAssassin does the same thing, only is more mature and extensible and uses a genetic algorithm rather than a back-propagation neural net.