Detection of Fraudulent Emails by Authorship Extraction
Fraudulent emails can be detected by extraction of authorship information from the contents of emails. This paper presents information extraction based on unique words from the emails. These unique words will be used as representative features to train Radial Basis Function (RBF). Final weights are obtained and subsequently used for testing. The percentage of identification of email authorship depends upon number of RBF centers and the type of functional words used for training RBF. One hundred and fifty authors with over one hundred files from the sent folder of Enron email dataset are considered.