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Let's Talk SPAM Join us Tuesday and Thursday (May 18th and 20th) at 12:30pm eastern to talk live with Ron Anderson about his recent review of 35 Anti-Spam hardware and software solutions. |
U.S. courts are the main venue for the big e-mail service providers' battles against spam. But legal proceedings are expensive; the process to identify John Doe defendants is a massive undertaking. And, like all things legal, these cases will take time, maybe years, to wend their way through the courts. If they're successful, we'll all benefit from these providers' efforts to shut down the big spam houses. But the vast majority of companies don't have the resources to wage this kind of war, and we don't need results at some unknown point in the future--we need relief today. So, for the rest of us, technology holds the key.
Technology hasn't conquered spam yet, but 70 percent of those responding to our poll said that antispam technology is at least somewhat effective, and nearly a quarter rated it as very effective. And almost 83 percent think the technology will get even better.
There's good reason for this optimism. Based on the technical answers available and the number of vendors developing new methods and combinations, the most difficult part of implementing an antispam solution may be parsing the choices. Our advice: Concentrate on the key metrics of accuracy and cost to narrow the field, then pilot-test each system to determine which is best for you. To help you make your choice, we invited 35 (yes, 35) vendors to participate in an accuracy shoot-out in our Syracuse University Real-World Labs®. The results of that testing and our analysis of the Top 10 vendors' features starts here.
From a technical perspective, accuracy is fundamental. Products that block the most spam without misidentifying legitimate mail (aka ham) as spam should move to the top of your short list of vendors. All the vendors publish their own accuracy ratings, but you'll need to make sure their definition is the same as yours. A simple formula takes the total number of mistakes a product makes when classifying mail--both spam classified as ham (false negative) or ham classified as spam (false positive)--and divides by the total number of messages.
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