Quantitative Analysis of Efficient Antispam Techniques

Abstract

While dynamic content-based filtering mechanisms for the identification of unsolicited commercial email (UCE, or more commonly "spam") have proven to be effective, these techniques require considerable computational resources. It is therefore highly desirable to reduce the number of emails that must be subjected to a content-based analysis. In this paper, a number of efficient techniques based on lower protocol level properties are analyzed using a large real-world data set. We show that combinations of several network-based filters can provide a computationally efficient pre-filtering mechanism at acceptable false-positive rates