Adaptive e-mail intention finding mechanism based on e-mail words social networks

Abstract

Through the rapid evaluation of spam, no fully successful solution for filtering spam has been found. However, the spammers still spread spam by using the same intentions such as advertising and phishing. In this investigation, we propose a mechanism of Email Words Social Network (EWSN) for profiling users' intentions related to interesting and uninteresting e-mails. An EWSN is constructed from the information in an individual user's mailbox, and expands e-mail information from the World Wide Web (WWW) via the search engine. Based on the web information and association rules among the words, words and relations are expanded as a words' social network. Via the EWSN, both interested and uninterested EWSNs can be constructed to analyze user intentions. Additionally, an efficiency detection mechanism based on the EWSN is proposed to classify e-mails. Finally, the adaptation algorithm of artificial immune system is applied to EWSN, which is thus adapted to follow the user's confirmed classification results. The experimental results indicate that the proposed system is very helpful for classifying spam e-mails by analyzing senders' intentions. Some ideas for analyzing interested nature of people, and profiling their backgrounds, are also presented. Copyright 2007 ACM.