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Spam Filter Most of the wired world has grown weary of e-mail invitations to enhance their manhood or buy a certificate allowing them to marry couples in Idaho, and the folks at Nettwerk Productions in Vancouver are no different.

But spam has been put in its place now, says Jason Currell, the record label's system administrator, thanks to technology from another Vancouver company, ActiveState Corp.

"We were getting complaints constantly," he says.

Nettwerk uses the company's PureMessage mail filtering tool to keep spam under wraps. While it still reaches the end User--Nettwerk also has offices in New York, Los Angeles and London, England--questionable e-mail is marked and sent to a specific folder. This is the default installation, says Currell, and in the off chance a real e-mail is deemed spam or a "false positive," users can search their spam folder if they didn't receive an expected important e-mail.

Before adopting PureMessage, Nettwerk tried a number of in-house techniques to combat spam, whether it was blocking certain e-mail servers or subscribing to the RBL--a "real-time blackhole list" which compiles addresses to be blocked and shares them with Internet users--to little effect. "It depends on the person, but I would get a hundred or so a day."

While PureMessage can do more than just defend inboxes against junk mail--it can filter both inbound and outbound e-mail --spam was the main reason Nettwerk installed PureMessage, says Currell. He says PureMessage's approach to dealing with spam made a lot of sense, and properly addressed the issue of false positives.

"Before these heuristic solutions were out there, there was no solution really."

It also sits on the mail server, so software doesn't have to be installed on individual computers.

For Nettwerk, spam is for the most part an annoyance. Currell says it's not all that worried about being liable for anything potentially offensive reaching users.

"We have a different sort of crowd here," says Currell. "We're not a law office. If somebody gets porn in their inbox, they just delete it. I can see how it would be a problem in other organizations."

According to Chris Kraft, ActiveState's product manager for PureMessage, the product came into being in 2000 as PureMX when the company realized it could leverage its expertise in Perl tools to create a platform that would enable developers to write filters for their e-mail. ActiveState soon realized it wasn't exactly what customers wanted.

"While the toolset is fine, there's a more immediate problem--that immediate problem being spam," he says.

Last November the product was rebranded PureMessage to better reflect what it does.

"While the demand today has largely been driven by virus and now spam," says Kraft, "the broader issue is large organizations don't have control over their e-mail stream.

"There's also concern around what's acceptable use of e-mail," Kraft adds. "Am I going to allow employees to determine what's appropriate and what's not in communicating with customers."

Marten Nelson, analyst with San Francisco-based Ferris Research, says many anti-spam tool vendors started providing general content filtering solutions and realized customers want anti-spam. Now those vendors are are simply repositioning the solutions they already have.

Then, there are vendors such as ActiveState, he says, that are investing in technology that does a good job of filtering spam, he says.

Spam is not the only reason enterprises are adapting mail filtering technology, but it's definitely what's driving the bulk of sales, says Nelson.

"When corporations evaluate anti-spam solutions, they realize there's more beyond resolving spam."


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The Goals of spam

The goal of spam is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good spam? It can be shown that there is no absolute “best” criterion which would be independent of the final aim of the spam. Consequently, it is the user which must supply this criterion, in such a way that the result of the spam will suit their needs.
For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection).  

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Who uses spam?

Many different types of organizations use spam as a vital part of the work. A sampling of these include:

• Marketing: finding groups of customers with similar behavior given a large database of customer data containing their properties and past buying records;
• Biology: classification of plants and animals given their features;
• Libraries: book ordering;
• Insurance: identifying groups of motor insurance policy holders with a high average claim cost; identifying frauds;
• City-planning: identifying groups of houses according to their house type, value and geographical location;
• Earthquake studies: spam observed earthquake epicenters to identify dangerous zones;
• WWW: document classification; spam weblog data to discover groups of similar access patterns.

 

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