Filtering Spam by Using Factors Hyperbolic Trees

Overview

Most of current anti-spam techniques, like the Bayesian anti-spam algorithm, primarily use lexical matching for filtering Unsolicited Bulk E-mails (UBE) and Unsolicited Commercial E-mails (UCE). However, precision of spam filtering is usually low when the lexical matching algorithms are used in real dynamic environments. For example, an E-mail of refrigerator advertisements is useful for most families, but it is useless for Eskimos. The lexical matching anti-spam algorithms cannot distinguish such processed E-mails that are junk to most people but are useful for others. The paper proposes Factors Hyperbolic Tree (FHT) based algorithm that, unlike the lexical matching algorithms, handles spam filtering in a dynamic environment by considering various relevant factors.