Abstract
E-mail fraud has become very prevalent in cyberspace and is currently a major technique utilized by cyber criminals to swindle victims. E-mail fraud is a category of spam or unsolicited bulk e-mail [1]. Spam filter research has been very active in combating e-mail spam. Spam filter research ranges from statistical methods for text categorization to newer methods of defining user preference ontologies to classify incoming e-mails. Much of these methods have limitations or an upper bound where they can be bypassed by simply misspelling, manipulating, or rephrasing the text.
The research proposed in this composition utilizes a new technique that uses a very powerful tool known as ontological semantics. Ontological semantics gives direct access to the texts meaning, which in turn will help accurately classify and categorize unsolicited bulk e-mails. This study will provide insight on less effective current spam filter techniques and discuss their limitations compared to the proposed method of
an ontological spam filter.
