%0 Journal Article %A Emad E Abdallah and A.F. Otoom and ArwaSaqer and Ola Abu-Aisheh and Diana Omari and Ghadeer Salem %D 2012 %J International Journal of Computer and Systems Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 63, 2012 %T Detecting Email Forgery using Random Forests and Naïve Bayes Classifiers %U https://publications.waset.org/pdf/3339 %V 63 %X As emails communications have no consistent authentication procedure to ensure the authenticity, we present an investigation analysis approach for detecting forged emails based on Random Forests and Naïve Bays classifiers. Instead of investigating the email headers, we use the body content to extract a unique writing style for all the possible suspects. Our approach consists of four main steps: (1) The cybercrime investigator extract different effective features including structural, lexical, linguistic, and syntactic evidence from previous emails for all the possible suspects, (2) The extracted features vectors are normalized to increase the accuracy rate. (3) The normalized features are then used to train the learning engine, (4) upon receiving the anonymous email (M); we apply the feature extraction process to produce a feature vector. Finally, using the machine learning classifiers the email is assigned to one of the suspects- whose writing style closely matches M. Experimental results on real data sets show the improved performance of the proposed method and the ability of identifying the authors with a very limited number of features. %P 309 - 313