@article{(Open Science Index):https://publications.waset.org/pdf/3339,
	  title     = {Detecting Email Forgery using Random Forests
and Naïve Bayes Classifiers},
	  author    = {Emad E Abdallah and  A.F. Otoom and  ArwaSaqer and  Ola Abu-Aisheh and  Diana Omari and  Ghadeer Salem},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Systems Engineering},
	  volume    = {6},
	  number    = {3},
	  year      = {2012},
	  pages     = {309 - 313},
	  ee        = {https://publications.waset.org/pdf/3339},
	  url   	= {https://publications.waset.org/vol/63},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 63, 2012},
	}