Authorship Categorization in Email Investigations using Fisher’S linear Discriminant method with radial Basis function

Email plays a vital role in faster communication. Lots of mails are sent to common public with falsified information that appears to be a realistic. It is mandatory to trace the origin of the email and the authors/systems responsible for generating such emails. Representative signatures of email are to be generated using lexical and syntactic based methods. The signature of each email has huge dimensions and is called a vector/pattern. In order to make it convenient for subsequent processing, the huge dimension of the signature is converted into 2-dimensional pattern using Fisher’s Linear Discriminant Function (FLD). The 2-dimensional patterns of the signatures of emails under consideration are used as training data for the Radial Basis Function (RBF) network which can learn non-linear data. The classification of email is very well achieved due to transformation by FLD and training by RBF. The proposed method helps in building signature database for accurate categorization in email forensics. The proposed combination of algorithms helps in clustering the different emails generated by an author or by a system.