Email Authorship Identification Using Radial Basis Function

Email authorship identification helps tracking fraudulent emails. This research proposes extraction 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 fifty authors with one hundred files from the sent folder of Enron database are considered. A total of 300 unique words of number of characters in each word ranging from 3 to 7 are considered. Training and Testing RBF are done by taking different length of words. The percentage of authorship identification ranges from 95% to 97%. Simulation shows the effectiveness of the proposed RBF network for email authorship identification.