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Arabic Spam filtering using Bayesian Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 79 - Number 7 |
| Year of Publication: 2013 |
| Authors: Abdulkareem Al-alwani, Majdi Beseiso |
10.5120/13752-1582
|
Abdulkareem Al-alwani, Majdi Beseiso . Arabic Spam filtering using Bayesian Model. International Journal of Computer Applications. 79, 7 ( October 2013), 11-14. DOI=10.5120/13752-1582
Abstract
Many of us are concerned about an onslaught of SPAM email. Spam has become major problem for the email communications. The number of spam mails is increasing daily – studies show that over 45-50% of all current email communication is spam, it is an ever-increasing problem and will reach up to 70% in coming years. The volume of non-English language spam is increasing day by day. The motivation for this research is to find a solution for the millions of internet users in the Arabic language struggling with hundreds of SPAMS being received every day in their mailbox. To filter this kind of messages, this research applied Bayesian Model which provides the framework for building intelligent learning system.
References
- Junod, John. 1997 Servers to spam: drop dead Computers and Security 16. 7 , 623-623.
- Email spam statistic. 2007, http://spamnation. info/stats/
- Hayati, P. , & Potdar, V. 2008. Evaluation of spam detection and prevention frameworks for email and image spam: a state of art. In Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services (pp. 520-527).
- Drucker, H. , Wu, D. , & Vapnik, V. N. 1999. Support vector machines for spam categorization. Neural Networks, IEEE Transactions on, 10(5), 1048-1054.
- Khreisat, L. 2006. Arabic text classification using N-gram frequency statistics a comparative study. In Conference on Data Mining| DMIN (Vol. 6, p. 79).
- Damashek, M. 1995. Gauging similarity with n-grams: Language-independent categorization of text. Science, 267(5199), 843-848.
- Getis, A. 2007. Reflections on spatial autocorrelation. Regional Science and Urban Economics, 37(4), 491-496
- Rajput, A. , & Toshniwal, D. Adaptive Spam Filtering based on Bayesian Algorithm.
- Qasem A. Al-Radaideh & Ahmed F. AlEroud. Arabic alert E-mail detection using rule based filter.
- Abu El-Khair, I. , 2006. Effects of Stop Words Elimination for Arabic Information Retrieval: A Comparative Study. International Journal of Computing & Information Sciences, Volume 4, Number 5, p. p. 119 – 133.
- Khorsi, A. 2007. An overview of content-based spam filtering techniques. Informatica (Slovenia), 31(3), 269-277.
- Jaramh, R. , Saleh, T. , Khattab, S. and Farag, I. , 2012. Arabic Email Spam Detection Techniques and Related Arabic Text Preprocessing Options: A Survey. Detecting Arabic Spam Web.
- Saad, M. , 2006. "The Impact of Text Preprocessing and Term Weighting on Arabic Text Classification", Master of Science, Computer Engineering, The Islamic University Gaza.
- Wahsheh, H. A. , Al-Kabi, M. N. , & Alsmadi, I. M. 2012. Spam Detection Methods for Arabic Web Pages. In First Taibah University International Conference on Computing and Information Technology-Information Systems ICCIT (pp. 486-490).
- Farmer, James John 2003. "3. 4 Specific Types of Spam" (FAQ). An FAQ for news. admin. net-abuse. email; Part 3: Understanding NANAE. Spam FAQ. Archived from the original.
- Sophos 2008. Sophos report reveals rising tide of spam in April–June 2008" (Press release).
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