Anti-Spam

Anti-Spam

There’s a lot of argument as to which “anti-spam” techniques are legitimately so called. In this article, I’d like to consider what constitutes an anti-spam technique in an ideal sense, then consider the various practiced approaches to spam mitigation in that light, drawing conclusions as to how we should frame the “anti-spam” discussion. An ideal anti-spam system rejects messages which are both bulk and unsolicited, letting pass those messages which are of specific personal relevance to the recipient (not “bulk"), and those which the recipient has expressly requested (not “unsolicited"). When phrased in these terms, spam filtering is obviously a task for a well-informed intelligent agent of immense sophistication—quite beyond our current ability to construct. Anything less is a weak approximation at best.

The system described so far is ideal in the sense that it keeps spam out of a recipient’s inbox, but it says nothing of network and computing resources consumed in the process. A system that accepts all mail and then discards the portion which is spam wastes significant resources on mail that will ultimately be discarded. This is the hidden cost of spam, and it can be arbitrarily large, since it depends on how much spam other parties send to the recipient. An ideal system must address this cost: it must not only be perfectly accurate, but also perfectly efficient. In the ideal case, each incoming spam is rejected at no cost to the recipient. Only under these conditions is the system guaranteed to scale under increasing spam load.

To address this, the hypothetical intelligent agent could operate at the sender’s system, preventing unwanted data from entering the network at all. Unfortunately this seems practically untenable for several obvious reasons, not the least of which is the cost of replicating the agent at every prospective sender. But in order for the agent to operate from the recipient’s system without the waste inherent in the “accept then drop” approach, it would need to engage with each potential sender in a very light-weight protocol for determining whether a candidate message is personally relevant or requested, prior to accepting the actual text. I can’t even imagine how a protocol would meet these requirements, let alone be reliable in the face of a hostile sender. The situation seems intractable.

If an ideal anti-spam system is technically possible at all, it’s firmly in the realm of science fiction for now.

How ANTI-SPAM works

This page has fifty randomly generated email addresses (refresh and new ones will appear). At the bottom of the page is a link to this page again, essentially reloading it for programs to collect more fake email addresses. Email collecting programs (spam bots) will be sent into an infinite loop by following the link at the bottom of the page and will get more and more fake email addresses stuck in their databases.

Spam is the electronic world's biggest problem. A fool proof method of filtering out spam does not yet exist, but we don't have to sit back and take it. Anti-Spam pages like this one make spamming less profitable and is our way to help FIGHT SPAM.

Anti-Spam Software

A spam filter is a program that is used to detect unsolicited and unwanted email and prevent those messages from getting to a user's inbox. Like other types of filtering programs, a spam filter looks for certain criteria on which it bases judgments. For example, the simplest and earliest versions (such as the one available with Microsoft's Hotmail) can be set to watch for particular words …

A spam filter is a program that is used to detect unsolicited and unwanted email and prevent those messages from getting to a user's inbox. Like other types of filtering programs, a spam filter looks for certain criteria on which it bases judgments. For example, the simplest and earliest versions (such as the one available with Microsoft's Hotmail) can be set to watch for particular words in the subject line of messages and to exclude these from the user's inbox. This method is not especially effective, too often omitting perfectly legitimate messages (these are called false positives) and letting actual spam through. More sophisticated programs, such as Bayesian filters or other heuristic filters, attempt to identify spam through suspicious word patterns or word frequency.