Spam or not-spam Clogging the gateway Neural networks Anti-spam glossary Lost advantage from automation Anti-spam and the feds What to ask your anti-spam tool vendors

Spam, the four-letter word of the virtual world, seems to be on everyone's lips these days, from rank-and-file workers to Congress and the British Parliament. The buzz occurs with good reason: Spam has leapt from a minor, annoying byproduct of e-mail to an epic business problem. Unsolicited e-mails are growing at a rate of 5% per month, according to a Kessler International survey. That means thousands of unwanted e-mails per week, often totaling 75% of the messages an enterprise e-mail gateway must process - while clogging downstream wires and servers, users say.

Spam on that scale also sucks up employee time; Nucleus Research reports that nuisance e-mail costs $874 per person annually in lost productivity (see how much it costs your company using our Spam Calculator). And with some messages so obscene as to make a merchant marine blush, much of the spam content is inappropriate for a business environment, if not outright illegal.

Government intervention has been discussed considerably as a solution, but network professionals aren't holding out for relief from legislation. Its effectiveness will be iffy at best (see story ). For the time being, exterminating the spam menace will remain the task of the network team. That's easier said than done. E-mail marketers constantly find ways to thwart existing e-mail filters. Anti-spam software vendors, in turn, create new filters intended to spot spammer's latest tricks. Not only must network executives frequently update software to get the latest filters, but the more filtering they switch on, the higher the chance that legitimate e-mail gets mislabeled and deleted as spam.

The latest crop of filters promises to stop this yo-yo cycle. These filters are based on "self-learning" or "machine-learning" technologies that attempt to adapt automatically to spammers' new tricks while protecting legitimate e-mail. Among machine-learning technologies in commercial spam filters, Bayesian filtering and neural networks are the most talked about, with Bayesian filtering generating a downright roar. In the past few months, this type of filter has been implemented in a growing number of anti-spam products, ranging from open source product SpamAssassin to an enterprise-class spam-detection module from start-up ProofPoint.