Using authorship analysis techniques in forensic analysis of electronic mails

As a result of rapid advances in information technology electronic mail has become one of today's most important communication tool. Electronic mail which provides conveniences to its user in many cases, is also an attractive environment for criminals. Malicious electronic mail whose actual owner is uncertain, is taking place in cyber crimes and authorship analysis has become necessary for determining the actual owner of this electronic mail. In this study 43 textual features were extracted from dataset of electronic mails which is obtained for 5 writers. These extracted textual features were processed with Artifical Neural Network (ANN), Support Vector Machines (SVM) and Decission Trees Method that are method of data mining classfication techniques in WEKA. As a result of the application. Decision Trees Method has been observed to be most succesful with F-measure rate of 83% in average for available dataset.

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