Do these numbers work for you?

By Joel Snyder
Network World, 09/15/03

Original Article on Network World Web Site

Testing anti-spam products fairly is not easy. We ran the best real-world test we could, and we think that it's a better test than has ever been run before. However, there are some very valid complaints about our methodology. The good news is that our numbers are under-reporting how well these products work. In an enterprise environment, you probably would see fewer false positives and a better spam reduction for almost all of these products.

The first part of our test that will give sub-optimal results is that the IP address of the sending system is not available to the spam gateway. Because we had to re-transmit all of the spam to all participants at once, all the messages appear to come from one place: our mail servers. Most of the products include some heuristics that are based on the IP address of the spam sender. These include blacklists and other statistical measures. In our test, all these features had to be disabled. Our own tests using the MAPS RBL+ list ( show that a well-run list will give approximately a 10% to 20% reduction in unwanted e-mail with a very low false-positive count. Other online lists have more draconian policies and will give a higher reduction in spam but also have a higher false-positive count.

The second under-reporting in our test is because of the lack of a feedback cycle. Our top-scoring products all let individual users manage their own settings, quarantines, blacklists and whitelists. Normally, a user might run with one of these anti-spam products for a few weeks and use that time to tune his settings and, more importantly, his whitelist. Once the whitelist has the most important correspondents in it, the settings can be turned to be more aggressive, filtering more spam with a lower false-positive count.

Some anti-spam products are customized so tightly that they won't even work properly without significant customization. Most of these are client-side products. Systems using Bayesian filtering work very well in domain-specific environments, but at the cost of having the user periodically train and re-train the system using both "good" and "unwanted" e-mail. They also redefine what is meant by spam. A well-trained filter will mark as unwanted e-mail from a mailing list that might be off-topic or on a sub-topic not interesting to the recipient. Spam is no longer just unwanted commercial bulk mail but can be anything that the recipient isn't interested in. No vendor submitted any server-side products that require this level of training.