Sunday 24th of September 2017
 

Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks


Ann Nosseir, Khaled Nagati and Islam Taj-Eddin

SPAM e-mails have a direct cost in terms of time, server storage space, network bandwidth consumptions and indirect costs to protect privacy and security breaches. Efforts have been done to create new filters techniques to block SPAM, however spammers have developed tactics to avoid these filters. A constant update to these techniques is required. This paper proposes a novel approach which is a characters-word-based technique. This approach uses a multi-neural networks classifier. Each neural network is trained based on a normalized weight obtained from the ASCII value of the word characters. Results of the experiment show high false positive and low true negative percentages.

Keywords: electronic mail (E-mail); spam filters; spam detection; Artificial Neural Network; stemming process.

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ABOUT THE AUTHORS

Ann Nosseir
Ph.D. in Computer Science, University of Strathclyde; currently is a Lecturer at The Faculty of Informatics and Computer Science, The British University in Egypt.

Khaled Nagati
Ph.D. in Computer Science, Joint supervision: American University in Cairo (AUC) and Cairo University; currently is an Associate Professor at The Faculty of Informatics and Computer Science, The British University in Egypt.

Islam Taj-Eddin
Ph.D. in computer Science, The Graduate School and University Center, The City University of New York; currently is a Lecturer at The Faculty of Informatics and Computer Science, The British University in Egypt.


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