Spam decisions on gray e-mail using personalized ontologies

ABSTRACT

E-mail is one of the most common communication methods among people on the Internet. However, the increase of e-mail misuse/abuse has resulted in an increasing volume of spam e-mail over recent years. As spammers always try to find a way to evade existing spam filters, new filters need to be developed to catch spam. A statistical learning filter is at the core of many commercial anti-spam filters. It can either be trained globally for all users, or personally for each user. Generally, globally-trained filters outperform personally-trained filters for both small and large collections of users under a real environment. However, globally-trained filters sometimes ignore personal data. Globally-trained filters cannot retain personal preferences and contexts as to whether a feature should be treated as an indicator of legitimate e-mail or spam. Gray e-mail is a message that could reasonably be considered either legitimate or spam. In this paper, a personalized ontology spam filter was implemented to make decisions for gray e-mail. In the future, by considering both global and personal ontology-based filters, we can show a significant improvement in overall performance.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Concept learning

Keywords:
e-mail classification, feature selection, gray e-mail, ontologies, spam filtering