Facebook Spam and Spam Filter Using Artificial Neural Networks
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Facebook Spam and Spam Filter Using Artificial Neural Networks

Authors: Fahim A., Mutahira N. Naseem

Abstract:

Spam is any unwanted electronic message or material in any form posted too many people. As the world is growing as global world, social networking sites play an important role in making world global providing people from different parts of the world a platform to meet and express their views. Among different social networking sites Facebook become the leading one. With increase in usage different users start abusive use of Facebook by posting or creating ways to post spam. This paper highlights the potential spam types nowadays Facebook users’ faces. This paper also provide the reason how user become victim to spam attack. A methodology is proposed in the end discusses how to handle different types of spam.

Keywords: Artificial neural networks, Facebook spam, social networking sites, spam filter.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1098954

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References:


[1] V.N. Vapnik, H. Druck, D. Wu, "Support Vector Machines for Spam Categorization", IEEE Transactions On Neural Networks, vol. 10 no.5 , pp. 1048-1054, Sep 1999.
[2] L. Lazzari, M. Mari, A. Poggi, "A collaborative and multi agent approach to e-mail filtering", IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’05), pp. 238-241, 2005
[3] G. K. Tak and S. Tapaswi “Query based approach towards spam attacks using artificial neural network” International Journal of Artificial Intelligence & Applications (IJAIA), vol.1, no.4, October 2010 DOI : 10.5121/ijaia.2010.1407 82
[4] A. Ho, A. Maiga and E. Aïmeur “Privacy Protection Issues in Social Networking Sites” IEEE, 2009
[5] Reza Ariaeinejad and AlirezaSadeghian “Spam Detection System: A New Approach Based on Interval Type-2 Fuzzy Sets” IEEE CCECE , Canada,2011
[6] F. Ahmed and M. Abulaish “An MCL-Based pproach for Spam Profile Detection in Online Social Networks”IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications 2012
[7] S. Dhanaraj and Dr. V. Karthikeyani “A Study on E-mail Image Spam Filtering Techniques” Proceedings of the 2013 International Conference on Pattern Recognition, nformatics and Mobile Engineering (PRIME), pp. 21-22,February 2013.
[8] A. Nosseir, K. Nagati and I. Taj-Eddin “Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks” IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 1, March 2013M. Young, The Techincal Writers Handbook.Mill Valley, CA: University Science, 1989.
[9] A. Kumar, S. K. Gupta, A. K. Rai and S. Sinha “Social Networking Sites and Their Security Issues” International Journal of Scientific and Research Publications, vol. 3, no 4, April 2013
[10] M. Vanetti, E. Binaghi, E. Ferrari, B. Carminati, and M. Carullo “A System to Filter Unwanted Messages from OSN User Walls” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 2, february 2013
[11] DolvaraGunatilaka “A Survey of Privacy and Security Issues in Social Networks”www.cse.wustl.edu/~jain/cse571-11/ftp/social/
[12] L. Bilge, T. Strufe, D. Balzarotti, and E. Kirda, “All your contacts are belong to us: automated identity theft attacks on social networks,” in Proceedings of the 18th International Co